Skip to content

p4fg/massdns-rs

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
.gitignore
.gitignore
 
 
README.md
README.md
 
 

Repository files navigation

massdns-rust

Initial prompt (o1-pro with Deep Research)

I have looked at the tool massdns available at https://github.com/blechschmidt/massdns
Help me create a rust-equivalent that has the same (or as close to) performance as possible.

Response

To create a Rust equivalent of massdns with similar performance, I need to clarify a few things:

  • Key Features: Are you looking to replicate all of massdns’s features (e.g., high-speed DNS resolution, brute force subdomain enumeration, etc.) or only specific parts?
  • Performance Goals: massdns is optimized for speed with a high degree of parallelism. Do you want the Rust version to achieve comparable speed using async Rust (e.g., Tokio)?
  • Dependencies: Do you prefer using a specific async runtime like Tokio, or would you like it to be dependency-light?
  • Output Format: massdns supports multiple output formats (JSON, list, etc.). Do you want all of them or just a particular format?
  • Concurrency Model: massdns uses raw sockets for high performance. Are you okay with using similar low-level networking in Rust?
  • Custom Features: Do you want any improvements over massdns, such as additional DNS query types or integration with other tools?

Let me know your preferences, and I can help plan the architecture and implementation!

Answer prompt (o1-pro with Deep Research)

i am ok with low-level networking. I want to replicate all features. Tokio can be used if that has enough performance.

Response

Research completed in 6m · 19 sources

Great! I will research how to implement a Rust equivalent of massdns with high-performance, low-level networking. I will focus on replicating all features of massdns while ensuring performance comparable to the original tool. I will explore using Tokio for concurrency and async networking, as well as alternative optimizations for high-speed DNS resolution.

I'll update you once I have a comprehensive plan for the implementation.

Implementation Plan for a Rust-Based MassDNS Equivalent

Overview of MassDNS and Goals

MassDNS is a high-performance DNS stub resolver for bulk lookups and subdomain reconnaissance (massdns | CyberSecTools). It can resolve millions of DNS queries in minutes by sending queries directly over UDP with massive concurrency (Puredns - Subdomain Bruteforcing Tool - Penetration Testing Tools, ML and Linux Tutorials). The goal is to create an equivalent tool in Rust that replicates all of MassDNS’s features and performance. This Rust implementation aims to maintain feature parity (raw socket DNS queries, subdomain bruteforce, multiple output formats, etc.) while leveraging Rust’s safety and clarity. The result should be a fast, efficient DNS batch resolver that is easier to maintain (memory-safe) without sacrificing speed compared to the C-based MassDNS.

Core Features to Replicate

To match MassDNS, the Rust tool must support the following features:

By ensuring all the above, the Rust version will mirror MassDNS in capability while ideally improving security and code maintainability.

Architecture and Concurrency Design

To achieve high performance, the Rust implementation will use an asynchronous, event-driven architecture. The design will be similar in spirit to MassDNS’s epoll-based loop (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools), but using Rust’s async runtime for clarity. Key aspects of the architecture include:

  • Tokio Runtime for Async I/O: We will use the Tokio library to manage asynchronous networking. Tokio provides a robust event loop (using epoll/kqueue under the hood) and lightweight tasks for concurrency (Asynchronous Programming and the Tokio Runtime: A Beginner's ...). This allows thousands of simultaneous UDP operations without spawning thousands of threads. The Rust tool’s concurrency model will mirror MassDNS’s non-blocking I/O approach (MassDNS uses epoll + callbacks in C (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools)) by leveraging Tokio’s async/await. Each UDP socket can be polled asynchronously for send/receive events, enabling high throughput on a single thread or across a thread pool.

  • Worker Tasks and Futures: Rather than one OS thread per query, we will spawn a limited number of async tasks to handle DNS queries. For example, one task could continuously send queries from a queue, while another task receives incoming responses. We might create N worker tasks (where N is the number of UDP sockets or CPU cores) that each handle a portion of the queries concurrently. This design avoids the overhead of spawning a new task per query (which would be excessive for millions of domains). Instead, tasks will reuse sockets and handle many queries over time, similar to how MassDNS reuses sockets for many lookups (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools).

  • Multiple UDP Sockets: The tool can open several UDP sockets (configurable via a --socket-count parameter, default 1, as in MassDNS (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration))). Each socket could be bound to a different source port (and optionally IP) to increase throughput. Having multiple sockets allows parallel I/O operations and can bypass single-socket limits (e.g., UDP packet per second limits or port-specific rate limits). In the Rust implementation, we might assign one Tokio task per socket to handle its send/receive events concurrently. All sockets will be registered with the runtime, and Tokio will notify when any socket has data, emulating MassDNS’s epoll on multiple sockets (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools).

  • Query Dispatch and HashMap for Tracking: We will maintain a concurrent data structure to track outstanding DNS queries, similar to MassDNS’s internal hashmap that maps query IDs to domain names (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). In Rust, this could be a HashMap<u16, QueryContext> protected by a mutex or a lock-free structure (since multiple tasks will access it). Each outgoing DNS query will have a 16-bit ID; we store an entry (ID -> domain/query info) in this map when sending. The QueryContext can include the domain name, the resolver used, the query type, and a timestamp for timeout. When a UDP response is received, the receiver task looks up the ID in the map to find which domain/query it corresponds to, then processes the result and removes the entry. This is analogous to how MassDNS matches responses to queries via transaction IDs. Rust’s ownership model ensures only one response will match and remove a given query entry (preventing race conditions).

  • Flow Control (Concurrency Limit): To avoid flooding the network beyond what we can handle, the implementation will enforce a maximum number of in-flight queries (like MassDNS’s --hashmap-size limit, default 10000 (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration))). We can use a Tokio semaphore or an async channel to throttle submissions. For instance, before sending a query, acquire a permit from a semaphore; when a response is received (or a query times out), release the permit. This ensures at most N queries are outstanding. Alternatively, use tokio_stream::StreamExt::buffer_unordered(N) on an input stream of domains to cap concurrent futures. This prevents memory overload and excessive packet loss.

  • Task Coordination: The main tasks in the program will be:

    • Input Reader: Reads domain names (or generates subdomains) from the input source (file or stdin) asynchronously and feeds them into the query queue.
    • Query Sender: Picks up domains from the queue, builds DNS query packets, and sends them via a UDP socket (round-robin across available sockets or resolvers). Could be one sender task per socket for simplicity.
    • Response Receiver: Listens on the UDP sockets for incoming DNS responses. Upon receiving a packet, parse the DNS header and ID, look up the corresponding query in the map, then handle the result (for output and to mark the query as completed).
    • Timeout Handler: A mechanism (possibly a background task or using Tokio’s sleep) to detect queries that didn’t get a response within a timeout. If a query times out, it should be marked failed and possibly retried or logged, and its slot freed. (MassDNS can retry queries a number of times on failure; we can allow a configurable retry count as well.)

    These components will run concurrently. For example, the receiver task will continuously process responses even as the sender is firing off new queries, enabling full pipeline utilization (no waiting for one query at a time). This design takes advantage of Rust’s ability to “fearlessly parallelize” code across threads safely (Speed of Rust vs C), something that would be harder to do in C without risking race conditions or complexity.

  • Multi-Threading vs Single-Threaded Event Loop: Tokio allows running the event loop on multiple threads (the multi-threaded runtime). We can leverage this to use multiple CPU cores for sending/receiving concurrently. This means our tasks (sender/receiver) might be scheduled on different threads, potentially increasing throughput on multi-core systems. In contrast, MassDNS’s default C implementation is single-threaded (one epoll loop) and uses multiple processes for parallelism when needed (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). With Rust, using threads within one process is safer (due to ownership and thread-safety guarantees) and lighter weight than full processes. By using a multi-threaded Tokio runtime, the Rust tool could handle more queries in parallel without the overhead of IPC or separate processes. This is an advantage where Rust can match or exceed C performance by easier scaling across CPU cores (Speed of Rust vs C). (We should also offer an option to run single-threaded if needed, to mimic exact MassDNS behavior or reduce context-switch overhead in some cases.)

In summary, the architecture uses async concurrency to replicate MassDNS’s high throughput design, with clear separation of concerns (input, network I/O, output) and safe shared state for tracking queries.

Raw Socket DNS Resolution in Rust

Direct DNS queries via raw UDP sockets are central to performance. The Rust implementation will use low-level socket operations to send DNS packets efficiently:

  • UDP Sockets: We will utilize Rust’s std::net::UdpSocket (or tokio::net::UdpSocket for async) to send and receive DNS packets. This allows sending queries directly to recursive DNS servers (from the provided resolvers list) without any intermediate resolver. Each socket will be bound to a local address/port. No TCP or system resolver calls will be used, only our own UDP packets (MassDNS also exclusively uses UDP for speed (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools)). On Linux, no special privileges are needed for UDP on ephemeral ports; the tool can bind to a high-numbered port and communicate with resolver IPs on port 53. (If we implement --bind-to for specific interfaces or low ports, it may require running as root or using CAP_NET_RAW, but that’s optional like MassDNS’s --root flag.)

  • Crafting DNS Packets: We will build DNS query packets manually or with a library. A DNS query consists of a header (with fields like ID, flags, question count) and a question section (name, type, class). For performance and control, constructing these packets with minimal overhead is important. One option is to use the Trust-DNS Proto crate or a similar DNS library to compose and parse messages. Trust-DNS’s low-level module can create a DNS message and serialize it to bytes easily. Alternatively, writing a small routine to build queries might be sufficient (e.g. to encode a domain name into the DNS wire format with labels, set the type field, etc.). This avoids overhead of full DNS libraries if we only need basic query/response handling. Parsing responses can likewise be done with a lightweight library like dns-parser or by manual parsing of the necessary fields (e.g. read the header, question, and resource record sections). The implementation should parse at least the answer records to output results. Using a library can ensure correctness for various DNS record types and reduce development time.

  • Raw Socket Performance Tweaks: We will configure socket options for high performance:

    • Increase the OS UDP buffers – Similar to MassDNS’s --rcvbuf and --sndbuf options (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration)), use socket2 crate or nix to call setsockopt and enlarge the receive buffer (SO_RCVBUF) and send buffer (SO_SNDBUF) sizes. Larger buffers help prevent packet drops when many responses come in quickly or when sending bursts of queries.
    • Enable SO_REUSEADDR/SO_REUSEPORT as needed – If multiple sockets or processes are used, these options allow reusing ports. For example, if the user sets a specific port to bind all sockets to (MassDNS’s --bind-to), SO_REUSEPORT lets multiple sockets listen on the same port across threads/processes.
    • For IPv6 randomization feature, if implementing, we might need to bind new sockets to randomly generated IPv6 addresses within the prefix for each query. This is complex to do on a per-query basis (opening a socket per query would be too slow). A simpler approach is to pre-compute a pool of IPv6 addresses from the prefix and instantiate a socket bound to each, then rotate through them for queries. This way, each query uses a socket with a different source IP. Managing dozens or hundreds of sockets for this feature is a challenge but could be optional for users who need it (MassDNS’s IPv6 rotation is a niche feature (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools)).

  • Batch Sending: To optimize throughput, we will explore batching system calls. Rust’s nix crate exposes the sendmmsg() system call, which allows sending multiple UDP messages in a single kernel call (nix::sys::socket::sendmmsg - Rust). We can accumulate a batch of DNS query packets (destined for possibly the same resolver or different ones) and send them together to reduce syscall overhead. For example, if 1000 queries are ready, sendmmsg can dispatch them in one go instead of 1000 separate sendto calls. Similarly, recvmmsg() can receive multiple packets at once. Using these could significantly improve performance in high-throughput scenarios (MassDNS in C might not use these calls explicitly, but it benefits from event-driven batching via epoll). In Rust, we need to use unsafe calls or crates for sendmmsg, but it’s doable and can be encapsulated in a safe abstraction. This is an advanced optimization – the initial implementation might send packets one by one asynchronously (which is often fast enough), and then batch sending can be added once correctness is verified.

  • Error Handling: The socket handling code must deal with network errors gracefully. For example, if a socket’s send buffer is full or a network error occurs, the code should catch the error and possibly retry. UDP sends might occasionally fail if the network is congested (though usually UDP just drops packets rather than erroring). On receive, we need to handle malformed responses or unrelated packets; non-DNS packets on that port can be ignored. Also, if using multiple resolvers, some may not respond or could be slow – we might implement logic to mark a resolver as unhealthy if many queries to it time out, and temporarily skip it (to maximize overall resolution speed).

By using raw UDP sockets with these methods, the Rust tool will achieve direct, high-speed DNS resolution akin to MassDNS, but with Rust’s strong type safety ensuring packet buffers and memory are handled correctly (no overflow or use-after-free issues that could happen in C).

Handling High Concurrency with Tokio

High concurrency is crucial for performance, and Tokio provides multiple ways to achieve it:

  • Async/Await for Concurrency: The Rust implementation will heavily utilize async/await to overlap I/O operations. When a DNS query is sent, the task can await the socket’s response without blocking the OS thread. Meanwhile, other tasks or future queries can proceed. This model allows tens of thousands of concurrent “in-flight” queries with minimal threads – similar to how MassDNS interleaves thousands of queries using epoll on one thread (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). In essence, each pending DNS query corresponds to a state in a state-machine rather than an actual thread.

  • Tokio’s Multi-Threaded Scheduler: By default, Tokio can run the async tasks on a thread pool (the size can be set to number of CPU cores, for example). This means send/receive processing can be parallelized. One thread might handle encoding and sending queries while another handles incoming packets, etc. We need to ensure thread-safe access to shared data (like the pending-queries HashMap) – fortunately, Rust makes this straightforward by requiring synchronization (e.g. an Arc<Mutex<..>>) for shared state across tasks. We will use an asynchronous mutex (Tokio’s Mutex or possibly DashMap for lock-free concurrent map) to guard the query state table. The slight overhead of locking is acceptable given the large I/O wait times, but we will minimize critical sections (e.g. lock just to insert or remove an entry, not for long operations).

  • Concurrency Patterns: Two possible patterns for managing concurrency in Rust:

    1. Producer-Consumer with Channel: The input reader task sends domain names into an async channel (queue). A fixed pool of worker tasks consumes from this channel. Each worker, upon receiving a domain, performs the DNS lookup (send query, wait for reply). This model is similar to having N threads in C performing lookups, but here they are lightweight tasks. The number of workers could correspond to number of sockets or be tuned. Workers can use select! or split futures to both send a query and simultaneously listen for its response within the same task context.
    2. Futures Unordered: Alternatively, use FuturesUnordered or for_each_concurrent to spawn a future for each domain up to a limit. For example, domains_stream.for_each_concurrent(max_concurrency, |domain| async { perform_query(domain).await }). This automatically caps the concurrency and simplifies logic – Tokio will ensure only max_concurrency queries run at a time. Each perform_query would handle sending the query and awaiting the response (perhaps with a timeout). This pattern is high-level but efficient; it uses internal task scheduling to keep the pipeline full. The downside is less explicit control over batching and socket distribution, but we can incorporate resolver selection in perform_query.

    We might combine patterns: e.g., use a small number of sender tasks reading from a channel (to batch sends), and a separate centralized receiver task for all responses. The design will be refined through testing to maximize throughput.

  • Async Timeouts and Retries: Tokio provides timers (tokio::time::delay_for or timeout) which we will use to implement query timeouts. When a query is sent, we can create a tokio::time::Timeout future that completes after a given duration if no response arrives. If the timeout triggers, we consider the query failed or retry it. MassDNS by default retries non-responsive queries multiple times (and considers certain DNS response codes as failures to retry) (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration)). We can allow a configurable retry count. The implementation might spawn a retry by re-enqueuing the domain to the sender (possibly using a different resolver the next time, unless --sticky is specified to stick to one resolver (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration))). Handling this asynchronously (maybe in the receiver task if it detects no answer within time) will be important for completeness.

  • Backpressure: The tool should also handle backpressure – if outputs are being written to disk and slow down, we shouldn’t infinitely queue results in memory. One way is to have the output writing in a separate task and use a bounded channel for results. If the channel backs up (i.e., output cannot keep up with the rate of findings), we could pause reading new domains or slow down sends. In practice, printing to a file is usually fast enough not to bottleneck until extremely high rates, but it’s a consideration. Tokio channels allow bounding capacity to apply backpressure naturally.

Overall, Tokio’s async facilities will let us achieve the massive concurrency needed. This approach mirrors Go’s goroutines used by ZDNS (another DNS blaster) and the epoll approach of MassDNS, but with safer, more structured code. Rust’s concurrency guarantees (no data races, thread-safe constructs) mean we can push the concurrency limits with confidence, whereas in C this required careful manual management (Speed of Rust vs C). The combination of Tokio and careful design should give performance on par with the C implementation while keeping the code maintainable.

Supporting Subdomain Enumeration and Output Formats

Beyond the core resolver engine, the Rust tool must include the additional features that make MassDNS useful for reconnaissance:

  • Subdomain Bruteforce Integration: In MassDNS, subdomain brute-forcing is often done by piping a generated list of hostnames into MassDNS or using the subbrute.py helper (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). For our implementation, we can integrate this functionality directly:

    • Provide a mode or flag (e.g. --wordlist <file> --domain <root>) that triggers generation of queries for every word in the wordlist appended to the root domain. For example, if root is "example.com" and wordlist has "www, mail, test", it will query "www.example.com", "mail.example.com", etc. This generation can be done on the fly (stream the wordlist file line by line, append the domain, and send to the query queue). This avoids storing millions of combinations in memory at once. Rust’s I/O libraries allow efficient line-by-line reading with BufReader. The pipeline (reading -> querying) can be fully streaming.
    • Alternatively, if the input is given as a file of subdomains (like MassDNS accepts a list of domains to resolve), we simply feed that in. The tool should be flexible: either accept a ready list of names (for compatibility with existing workflows) or accept a wordlist + root to generate the list internally. The latter is a user-experience improvement (one less script to run).
    • Ensure that the concurrency control and other logic doesn’t change whether the domains came from a file or are generated. From the perspective of the resolver core, it’s just a stream of domain names.

  • Multiple Output Formats: We will implement MassDNS’s output flags to produce results in various formats (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration)):

  • Output Performance: We need to handle output efficiently because printing or writing to files can be a bottleneck at high query rates:

    • Use buffered writing (BufWriter) for file output or ensure stdout is line-buffered to reduce syscall overhead.
    • Construct output lines/JSON in memory and write in one go rather than many small writes (e.g., accumulate the string for one DNS response completely, then print).
    • Offload output formatting to a dedicated thread or task. For instance, the receiver task, after parsing a response, could send a smaller structured result (like domain and IPs) into an output queue. A single output task can take from this queue and handle formatting and writing. This way, the network handling tasks aren’t slowed by disk I/O. Because formatting JSON or text is CPU work, doing it on a separate thread could also parallelize CPU usage (one core formatting output while another core handles network). Rust’s async and threads can intermix, or we could use a standard thread via std::thread::spawn for output if easier.
    • Ensure thread-safe writes. If multiple tasks might attempt to write to stdout or the same file, protect it with a mutex or channel as mentioned. It’s simpler to funnel all writes through one task/thread to avoid interleaving lines.

  • Verification and Filtering: MassDNS has options like --verify-ip (to verify that the IP in a response matches the question name, preventing certain spoofing) and prints the resolver address in outputs so users can filter out bad resolvers (GitHub - blechschmidt/massdns: A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration)). In our plan, we should include:

By implementing these features, the Rust tool will not only match MassDNS in functionality but also integrate more tightly (e.g., built-in subdomain generation). Each output format and feature will be carefully tested to ensure consistency with MassDNS’s behavior, so that users can drop-in replace MassDNS with this Rust version in their workflows.

Code Structure and Dependencies

Developing this tool in Rust will require choosing the right libraries and structuring the code for clarity and performance. Here is the proposed code organization and key dependencies:

  • Project Structure: Use a binary crate (e.g., with Cargo). The code can be organized into modules:

    • main.rs or lib.rs with the entrypoint, argument parsing, and high-level coordination.
    • dns.rs module for DNS packet encoding/decoding (could wrap an external crate or contain custom routines).
    • resolver.rs for the core logic of sending queries and receiving answers (this could contain the async tasks, data structures like the pending-query map, etc.).
    • output.rs for formatting and writing output in various formats.
    • subdomain.rs (optional) for handling wordlist-based subdomain generation.
    • config.rs for defining a struct of configuration options (parsed from CLI flags) that can be shared across modules (like concurrency limit, output mode flags, etc.).

    This separation keeps code clear: e.g., all DNS protocol specifics are in one place, which aids maintainability.

  • CLI Parsing: Use the Clap crate (or similar, e.g., StructOpt which is now part of Clap) to parse command-line arguments. MassDNS has many flags; Clap can make defining these straightforward. For instance, flags for -r/--resolvers <file>, -t/--type <record_type>, -o/--output <formatFlags>, -w/--outfile <path>, --retry <n>, --socket-count <n>, etc. Clap also allows providing help messages, which we can base on MassDNS’s usage text. This ensures the Rust tool is user-friendly and familiar in invocation.

  • Asynchronous Runtime: Tokio will be the primary dependency for async support (as discussed). We will include tokio = { version = "1", features = ["full"] } to get TCP/UDP, time, etc., or a more minimal feature set if we want to slim it down. Tokio’s overhead is small relative to the task at hand and it's a proven library for high-performance networking in Rust.

  • DNS packet handling: For DNS protocol, we have options:

    • trust-dns-proto: A crate from the Trust-DNS project that provides DNS message parsing and construction. This saves us from implementing the DNS wire format and supports many record types. We can use trust_dns_proto::op::Message to construct queries and parse responses. It is asynchronous-agnostic (just data structures). One consideration: trust-dns-proto might allocate more or be slightly heavy if we create a Message for each query; we should measure and possibly reuse message objects. But given clarity is a goal, using a well-tested library here is wise.
    • dns-parser: A lightweight no_std DNS parser for Rust. It allows parsing bytes into DNS structures and constructing questions. It might be more manual but could be faster or use less memory.
    • Custom: Implement minimal DNS packing/unpacking ourselves. For example, writing a function to encode a domain name into labels, and using a byte buffer for the query. This is quite doable (DNS header is only 12 bytes). We could create a struct DnsHeader and DnsQuestion and implement a to_bytes() method. Similarly, parse just what we need from responses (e.g., we might just look for the answer RRs of certain types). Custom implementation gives full control over allocations (we can reuse a single buffer for many queries, etc.), which could improve performance. However, it requires careful attention to DNS specs and might be error-prone for less common record types.

    A good compromise is using a library for parsing (ensuring we correctly handle all cases) and possibly custom-building the queries (since that’s straightforward). We might start with trust-dns-proto for simplicity, and then profile the performance; if it’s a bottleneck, consider optimizing that part.

  • Output formatting: For JSON output, use Serde and serde_json. Define a struct like DnsAnswerJson { name: String, resolver: IpAddr, status: String, answers: Vec<AnswerRecord>, ... } and derive Serialize. Then each result can be output with serde_json::to_string(&record) to get the JSON line. This is much easier than manual JSON string building and less error-prone. The performance of serde_json is quite good in Rust (being written in Rust and optimized).

    For text outputs, we can use Rust’s standard format! macro or std::fmt to assemble lines. This is fine for moderate amounts of output. If performance becomes an issue (in extreme cases where formatting overhead is significant), we might consider more efficient concatenation (like using String::push_str on a pre-allocated buffer, or writing directly to the BufWriter with write! to avoid intermediate strings). But clarity suggests using format! or small helper functions for each format type.

  • Networking libraries: While Tokio covers UDP, we might also incorporate socket2 for advanced socket setup (as mentioned for buffer sizes, etc.). Socket2 provides a nicer API to set socket options on std::net::UdpSocket before handing it to Tokio. We can for example: create a socket, set reuseaddr, bind to an IP:port, set recv buffer, then convert it into a Tokio UdpSocket via UdpSocket::from_std. The pnet crate (packet network) is another option if raw Ethernet frames were needed, but here we stick to UDP/IP which the OS can handle for us, so pnet is unnecessary.

  • Logging and Debugging: Use a logging library (like env_logger or log) to output debug info if needed (e.g., if -v verbose flag is set). This can help in development to trace how many queries are in flight, etc., without always printing to output (which could interfere with results). For example, log the number of responses received per second or when retries happen, etc., which can be toggled on for troubleshooting.

  • Testing: Write tests for individual components:

    • Unit tests for DNS packet encoding/decoding (ensure a query we build can be parsed by our parser and vice versa).
    • Maybe an integration test with a real DNS server (if possible, send a known query to 8.8.8.8 and verify we parse the answer).
    • Tests for the subdomain wordlist expansion logic.
    • Tests for output format (given a synthesized DNS response struct, check that the formatted output matches expected string or JSON).

    Using Rust’s testing framework can catch regressions early and ensure we truly replicate MassDNS’s behavior.

  • Performance Testing Tools: We should also plan how to benchmark the performance against MassDNS. Possibly include a mode or separate program to generate a large list of dummy domains and measure queries per second, CPU usage, etc., using the Rust tool vs MassDNS. This will guide optimizations.

By leveraging these dependencies and organizing code modularly, the implementation will remain clear and maintainable. Rust’s expressive type system and libraries will reduce the likelihood of certain bugs (no buffer overflows when crafting packets, thanks to safe APIs, and easier CLI parsing than a C manual approach). The code clarity should be better than the C equivalent, fulfilling the goal of maintainability: as one commenter noted, “Rust developers can probably write solid code... easier to extend without adding security holes” (A year of Rust and DNS | Hacker News).

DNS Query Processing Optimizations

To achieve speed comparable to MassDNS (which is highly optimized C), we must incorporate various optimizations in the Rust implementation:

  • Socket Reuse and Minimal Overhead: As emphasized earlier, reusing a small number of UDP sockets for all queries (rather than creating sockets per query) is crucial. Both MassDNS and ZDNS reuse sockets extensively for performance (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). We will open sockets once and use them for the entire run, avoiding continuous open/close costs. Additionally, we avoid context switching by using async tasks rather than constantly spawning threads. The overhead of Tokio’s task scheduling is quite low, and much of the waiting time is in the OS network stack.

  • Batching and Syscall Reduction: The use of sendmmsg and recvmmsg can drastically reduce the number of system calls when dealing with thousands of packets. Each syscall has fixed overhead, so sending 100 packets in one call can be significantly faster than 100 individual calls. We will implement optional batching in the send loop:

    • For example, accumulate outgoing DNS queries into an array of mmsghdr structures and call sendmmsg. This could be done whenever the number of pending sends reaches a threshold or at a fixed tick (e.g., send whatever is batched every 1ms). We can cite that sendmmsg is an extension that enables sending multiple messages in one syscall (nix::sys::socket::sendmmsg - Rust), and using it should yield better throughput on Linux.
    • Similarly, recvmmsg could be used in the receive task to pull in a batch of responses. However, since our receive loop is event-driven (we react when the socket is readable), we might simply read packets in a loop until the socket is dry (using recv_from in a loop) which already batches naturally: once epoll indicates readability, likely multiple packets are queued in the socket buffer, and our loop can drain them. This approach approximates recvmmsg behavior but in user-space.
    • Note: These optimizations might be platform-specific (sendmmsg/recvmmsg are Linux-specific). We can abstract them so that on other OS (Windows, etc.), it falls back to a loop of send/send (since performance on Windows might not reach Linux levels anyway). The design should not require these system calls, but conditionally use them if available.

  • Memory Management: Avoiding excessive allocation is important:

    • We can reuse buffers for sending queries. For instance, allocate a Vec<u8> of sufficient size (DNS packets are max 512 bytes for UDP by default, unless EDNS extended which we may not need). We can construct query packets in-place in this buffer for each query, just modifying the relevant bytes (transaction ID, question name, etc.). This prevents allocating a new vector for every query. A pool of buffers could be maintained if we do multi-threaded sending.
    • For receiving, reuse a buffer or a pool of buffers to receive packets. For example, keep a Vec<[u8; 512]> for receiving (or even use a single buffer if processing one packet at a time). After parsing a packet, we can reuse that buffer for the next receive (since we will extract necessary info immediately).
    • Using zero-copy parsing where possible: If using a library like dns-parser, it can often take a reference to the original packet bytes for names, etc., avoiding copying data. We should be mindful to not clone large data unnecessarily. For output, we might copy data (like an IP string) and that’s fine, but we shouldn’t, say, copy the entire packet just to parse it.

  • Parallelism and CPU Utilization: Make sure the solution is not bottlenecked on a single CPU core. If using Tokio multi-thread runtime, spread work across threads. We might pin certain tasks to certain threads if needed (Tokio doesn’t natively pin tasks, but if each socket’s task is independent, the OS may schedule them on different cores). Also, parsing and formatting can be CPU heavy for tens of thousands of responses per second; distributing those tasks across cores is beneficial. Rust’s ability to parallelize safely can give us an edge: “Rust is low-level enough that it can be optimized for max performance as well as C... Rust programs also optimize quite well, sometimes better than C... and can fearlessly parallelize code” (Speed of Rust vs C). In practice, this means we can comfortably use multiple threads for different parts of the workload (network, parsing, writing) without introducing data races or Heisenbugs, which might deter such optimizations in C.

  • Optimized Algorithms: Use efficient algorithms and data structures:

    • The HashMap for pending queries should use a good hashing algorithm (the default Rust HashMap is fine, but we could consider a faster hash like AHash via the hashbrown crate for large volumes of keys). However, given at most ~10000 entries by default, a standard hashmap is likely fine.
    • Resolver selection can be done with a simple round-robin index increment, which is O(1). If a resolver fails often, we could mark it and skip (maybe move it to a “slow” list) – but that’s an enhancement.
    • Reading the input file can be a bottleneck if it’s huge (millions of lines). We should use buffered reads and perhaps even memory-map the file if extremely large. However, reading from disk is likely slower than sending DNS queries in many cases, so this could be a limiting factor in end-to-end runtime. We might encourage users to use SSDs or ensure the wordlist is cached in memory for best performance. The tool itself can only do so much – but non-blocking I/O for reading using a separate thread or async file I/O (Tokio has async file capabilities) could keep the CPU free to handle network.

  • Avoiding Unnecessary Work: For example, if output format is “list” (just domain names), we don’t need to fully parse all answer records, we only need to know if the query succeeded and maybe one answer. We could short-circuit parsing to improve speed. Similarly, if only A records are requested, we don’t need to process other record types in the answer or authority sections. A smart optimization is to tailor the parsing detail to the output needed:

    • In list mode, just check the DNS header for an NOERROR status and answer count > 0, and extract the answer name/IP.
    • In full mode, parse everything (which is slower but only on user request).
    • In JSON mode, likely parse into a structured form. This kind of conditional parsing can save CPU cycles. It complicates code a bit, but can be encapsulated (e.g., parse minimally, then if full output, parse the rest).

  • Comparison to C (MassDNS) Implementation: MassDNS in C achieves its speed through low-level optimizations (single-threaded epoll loop, non-blocking sockets, minimal per-packet processing in C). Our Rust implementation, if written carefully, should match these steps. The compiled Rust code (in Release mode) will optimize the high-level constructs to something very similar to C loops and function calls. For instance, the async state machine might compile down to a loop that polls sockets not unlike what MassDNS does. There might be some overhead from the runtime (slightly more bookkeeping than a raw epoll) and from safety checks (index bounds checks, etc.), but these are generally negligible or can be optimized out by the compiler. In scenarios where performance-critical sections need it, we can use unsafe to remove bounds checks or do manual pointer operations, but only if profiling shows a real need. Often, Rust’s zero-cost abstractions live up to their name, giving us the speed of C with the ergonomics of a higher-level language.

  • Memory footprint: Ensure we free resources when no longer needed. For example, the pending query map entries are removed as soon as responses come. Large buffers or vectors should be reused or dropped after use. Rust’s ownership model will help ensure nothing “leaks” unexpectedly. Also, using Vec::with_capacity to pre-allocate expected sizes (like for output strings or for storing results if needed) can avoid reallocation churn.

In essence, we plan to profile and iterate on performance: start with a clear, correct implementation using the tools above, then measure QPS (queries per second) and resource usage compared to MassDNS. If the Rust version is slower, identify hotspots (using profilers or instrumentation) and optimize those (maybe by using a more efficient algorithm or adding an unsafe block with a proven safe optimization). Given that “Rust can be optimized for maximum performance just as well as C” when needed (Speed of Rust vs C), we expect to reach parity. Additionally, Rust’s strength in concurrency might even allow exceeding MassDNS performance on multi-core systems, by doing work in parallel that MassDNS (in one thread) does sequentially.

Rust vs. C Performance Considerations

When comparing a Rust implementation to the original C-based MassDNS, a few points stand out:

  • Safety vs. Performance: Rust’s safety guarantees (e.g., bound checks, option handling instead of NULL, etc.) have a small runtime cost in some cases. However, in a tight loop like sending/receiving packets, these costs can be minimized. Many of Rust’s safety checks are eliminated by the compiler in optimized builds. For example, iterating over a vector in Rust will not do bounds checking on each access if the compiler can prove the loop limits (Speed of Rust vs C). We will ensure to write loops in an idiomatic way that the compiler can optimize (like using iterators or indexing patterns it can understand). In scenarios where we need to manually unroll or use pointer arithmetic, we can do so in unsafe blocks, but only if needed. The benefit of staying safe is we avoid common C bugs (buffer overruns, misuse of freed memory) without a performance penalty in most cases.

  • Concurrency and Parallelism: As noted, Rust makes it easier to use multiple threads without data races. MassDNS chose a single-thread + epoll model to avoid the complexity of multi-threaded C (and uses multi-processing to scale). In Rust, we can confidently use multi-threading. This means on a machine with, say, 4 cores, the Rust tool could potentially handle more queries per second by distributing the work, whereas MassDNS might have to be run as 4 separate processes (which is less efficient and harder to coordinate output). The ability to scale with threads is a major performance win for Rust. The overhead of thread synchronization (Arc, Mutex) is considered, but with careful design (only short critical sections, lots of independent work), it should not negate the multi-core advantage. A Hacker News discussion pointed out that Rust’s fearless concurrency means you can parallelize tasks that in C you might avoid due to risk, making Rust “more mature” for parallel execution (Speed of Rust vs C).

  • Network I/O Efficiency: At the system call level, both Rust and C ultimately use the same syscalls (e.g., sendto, recvfrom, epoll wait). Our Rust code via Tokio will use epoll (on Linux) just like MassDNS (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). The difference is MassDNS calls these syscalls directly in C, whereas Rust calls them through the Tokio runtime and the futures mechanism. Tokio is highly optimized in this regard, and the overhead of polling and waking tasks is minimal (Tokio’s scheduler has been benchmarked and improved significantly, even achieving ~10 million events per second in some tests (Making the Tokio scheduler 10x faster - An asynchronous Rust runtime)). We anticipate that the event handling overhead in Rust will be in the same ballpark as MassDNS’s manual event loop.

  • Memory Usage: Rust programs sometimes use more memory than equivalent C, partly due to runtime structures and less fine-grained control of allocations. Our tool should be mindful: e.g., each future or task has some overhead on the heap (stack frames for async state). With tens of thousands of concurrent futures, this could add up (Tokio tasks are quite small, but maybe on the order of a few hundred bytes each). In C, MassDNS maintains a hashmap of queries and uses a fixed-size array for events from epoll; its memory footprint is very tight. We might accept a moderately higher memory usage in Rust for the benefit of clarity (e.g., the HashMap might have overhead, or using Vec for buffers). However, this is usually not a problem unless handling millions of concurrent queries (which we won’t do — 10k is default, or even if we allowed 50k, it’s fine on a modern system with few tens of MB overhead). If needed, we can tune data structures or use arenas to allocate many small objects together to reduce per-object overhead.

  • Validation of Performance: We will compare against MassDNS by measuring:

    • Queries per second throughput for a large list (with a fast resolver like a local unbound or Cloudflare 1.1.1.1). We expect to reach similar QPS. If we see Rust is behind, we’ll investigate if it’s CPU-bound (profile CPU) or I/O-bound (perhaps hitting a cap like socket buffer limit).
    • CPU usage: Ideally, for a given throughput, Rust should not consume significantly more CPU than C. If it uses a bit more due to extra safety checks or abstraction overhead, that might be acceptable given typical environments (the user likely cares more about throughput than a few extra CPU cycles). But we aim to be close, maybe within 10-20% of C CPU usage, and hopefully equal or better when using multi-threading.
    • Latency: Each individual DNS lookup’s latency is mostly network-bound, but the added overhead of the runtime might add microseconds. This is usually negligible in the context of network latency (~tens of milliseconds). Still, we ensure no major delays in processing loop.

  • Edge Cases: One advantage of Rust is if any unexpected condition occurs (like a buffer overflow attempt by a malicious DNS response), the Rust program is less likely to suffer a security issue. C might crash or worse on a bad packet; Rust will panic safely or handle the error. In terms of performance, this means the Rust tool might be more robust under weird conditions (not exactly a performance metric, but a reliability metric). That reliability allows running at scale continuously without mysterious crashes, effectively improving sustained performance over time.

In conclusion, while C and Rust can be equally performant with optimal code, Rust provides a development experience where achieving that performance is easier and safer. As one analysis summarized, “Rust is low-level enough to be optimized as well as C… and Rust programs also optimize quite well, sometimes better than C” (Speed of Rust vs C). The planned optimizations and careful use of Rust’s features aim to ensure this DNS tool lives up to MassDNS’s speed. Any trade-offs (like slightly higher memory usage) are justified by the gains in security and code maintainability.

Potential Challenges and Mitigations

Implementing a MassDNS equivalent in Rust will come with some challenges. We identify these issues and how to address them:

  • Asynchronous Complexity: Writing a highly concurrent async application is complex. Ensuring that the query tracking, timeouts, and retries all work correctly without race conditions requires careful design. We must be cautious with the shared state (the pending queries map) – e.g., making sure that a response is not processed after a timeout caused the query to be considered failed (to avoid double freeing a slot). Using Rust’s concurrency primitives and extensive testing (with simulated delays, etc.) will be important. Tokio’s primitives (Mutex, channels) will help, but deadlocks or starvation bugs could occur if we design the locking wrong. Mitigation: keep the locking granular (e.g., lock only when accessing the map) and avoid holding locks while awaiting. Also consider using lock-free structures like DashMap which allow concurrent insert/remove without explicit locking.

  • DNS Protocol Nuances: DNS can be more complex than just A record queries. Challenges include:

    • Handling responses that are larger than 512 bytes (EDNS0, DNSSEC, etc.) which get truncated (TC flag) over UDP. MassDNS likely just prints whatever comes and doesn’t follow up via TCP (since it’s a stub resolver focused on speed, not completeness). We can decide to mimic that: if a response is truncated, just note it (or ignore extra data). Or we could attempt a TCP query for completeness, but that would severely impact performance (and MassDNS doesn’t do it by default). So we will likely skip TCP fallback to maintain performance, documenting that behavior.
    • Parsing various record types uniformly. If using a library, this is easier. If doing manually, we must handle pointer compression in DNS names (which is a bit tricky but well-documented). We should test with different record types (MX, TXT, etc.) to ensure our parser doesn’t break.
    • Internationalized domain names (IDN) – MassDNS probably doesn’t handle punycode conversion; it expects the input list to be in punycode if needed. We can do the same: assume domain inputs are ASCII/punycode. If needed, mention that Unicode domain names should be converted by the user or we can optionally integrate a crate to do it.
    • CNAMEs and multiple query types: If a user requests an A record, but the answer is a CNAME chain, we should ideally resolve the chain. MassDNS does not iterate on CNAMEs (it just returns the CNAME record and possibly the final A if the resolver provided it). Since we rely on recursive resolvers, they often return the full chain. We will just output what’s in the response.

  • Rate Limiting and DNS Server Behavior: When blasting many queries, some public DNS resolvers will start dropping or rate-limiting requests (e.g., returning SERVFAIL). MassDNS mitigates this by allowing multiple resolvers and IPv6 source randomization (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools) (ZDNS vs MassDNS: A Comparison of DNS Measurement Tools). We should test how our tool behaves when using a single vs multiple resolvers. If using multiple, ensure we distribute queries evenly. If a resolver starts failing, the output will show failures – that’s expected. One challenge is that too high concurrency can overwhelm even the client machine’s network stack (packet loss). The concurrency default of 10k might be too high for some environments; we should allow tuning and maybe recommend a sensible default or auto-tune based on the number of resolvers (e.g., 1000 queries per resolver concurrently). Mitigation: provide guidelines in documentation and maybe a warning if using extremely high concurrency without many resolvers.

  • Memory and Resource Limits: Creating thousands of tasks or holding thousands of sockets (if IPv6 rotation is used heavily) can hit OS limits (file descriptors, memory). We will need to ensure the tool doesn’t exhaust system resources:

    • File descriptors: Each UDP socket uses one FD. If we did something extreme like binding thousands of IPv6 addresses, we could run out of FDs. We should enforce a reasonable limit or use a smaller pool of sockets for IPv6 rotation.
    • UDP port exhaustion: If we reused only one socket, all queries use that source port. This is normally fine, but theoretically if we exceed 65k outstanding queries on one port, we’d exhaust DNS ID space and risk ID collisions. Our concurrency limit is below that by default (10k), so it’s fine. But if a user sets a very high concurrency on one socket, collisions could occur (two queries with same ID outstanding). We can avoid that by either limiting concurrency per socket to < 65535 (which is huge anyway), or by using multiple sockets when higher concurrency is needed. Document this or internally manage it (e.g., automatically use more sockets if concurrency > 60k, distributing IDs across them). This is an edge case, but worth noting to maintain correctness.
    • CPU overuse: If run on a machine with insufficient CPU, any high-performance tool could cause drops. We might include a runtime status output (MassDNS has a status indicator) to show queries sent/received, which can hint if performance is suffering. A challenge in async context is updating a status line – but we could use an atomic counter of sent queries and a separate thread to print stats every second (to stderr).

  • Cross-Platform Issues: MassDNS is typically run on Linux. Our Rust tool should compile on Linux easily. Windows support is a question – Tokio does support UDP on Windows, but raw socket behaviors differ. The performance might be lower on Windows due to lack of sendmmsg and a different I/O completion model. If targeting Windows, we may need to adjust (e.g., using multiple send_to calls, and IOCP via Tokio works but perhaps not as optimized for thousands of tiny UDP sends). We should test on Windows and Mac, but primary target is likely Linux. If some features (like IPv6 raw binding) require socket2 with flags not supported on Windows, we might cfg-gate them. This is mostly a minor challenge since Rust abstractions handle a lot cross-platform.

  • Development Time: Reimplementing all features of MassDNS is non-trivial. There is a risk of scope creep (adding extras like wildcard filtering, etc.). We should prioritize core functionality and get a working high-performance resolver first, then add bells and whistles. A possible plan:

    1. Implement basic async DNS sending and receiving with one socket, one output format (e.g., simple text).
    2. Verify performance and correctness on that basic version.
    3. Add concurrency control, multiple sockets, multiple resolvers support.
    4. Add other output formats (JSON, etc.) and subdomain wordlist input.
    5. Add less critical features (binary output, IPv6 src, etc.) and thorough CLI options. Each step can be tested. Doing this iteratively helps isolate bugs. The challenge is ensuring that at each step the performance remains on track and that adding features (which can add overhead) doesn’t degrade the primary goal.

  • Testing at Scale: Simulating millions of queries to truly test the tool might require significant infrastructure (or at least time). We might rely on smaller samples and assume linear scaling. However, some bugs only appear at scale (e.g., hitting a buffer limit after a certain number of queries). We should include stress testing in a controlled environment (maybe set up a local DNS server that simply echoes requests, and throw 1e6 queries at it). Observing memory usage and stability during such a test is crucial.

  • Community and Maintenance: If this tool is to replace MassDNS, it will likely attract users who will use it in various ways. We should be prepared to handle feature requests or edge case reports (for example, “MassDNS allowed X, but RustDNS doesn’t” or differences in output that break someone's parsing scripts). Ensuring we documented any intentional differences (or better, avoiding differences altogether) is important for adoption.

In addressing these challenges, we lean on Rust’s strengths: robust testing, clear compile-time errors for many issues, and the ability to refactor safely if something isn’t working. The end result should be a Rust-powered DNS blaster that is comparable in speed to MassDNS, but more robust. With proper planning and optimization, the implementation will meet the user’s needs for high-performance DNS enumeration while being easier to trust and extend in the long run.

Prompt (o1-pro)

Can you give me the sourcecode for this please?

Response

Request for o1-pro
Reasoned for 2m 35s

Below is an example, reference-only Rust implementation that demonstrates a highly concurrent DNS stub resolver similar in spirit to massdns. This code shows how you can:

  • Send raw UDP DNS queries to multiple resolvers at high concurrency.
  • Brute force subdomains from a wordlist.
  • Handle asynchronous I/O and timeouts with Tokio.
  • Produce simple text output of DNS results.

Important:

  1. This is not an official one-to-one massdns port. It is sample code that implements the core ideas in Rust: raw DNS querying, concurrency, subdomain enumeration, multiple resolvers, etc.
  2. It does not include all massdns features (e.g., advanced output modes like JSON or binary, IPv6 random source addresses, specialized wildcard detection, etc.). You can expand on this skeleton to match all of massdns’s features.
  3. Code is provided under the MIT license at the end. Feel free to modify and adapt as you wish.
  4. While this code attempts to be correct and reasonably performant, it is not thoroughly tested or security audited. Use at your own risk in production scenarios.

Cargo.toml

Create a new Rust project (e.g., cargo new massdns-rs), then in Cargo.toml add these lines:

[package]
name = "massdns-rs"
version = "0.1.0"
edition = "2021"

[dependencies]
tokio = { version = "1", features = ["full"] }
clap = { version = "4.0", features = ["derive"] }
anyhow = "1.0"
socket2 = "0.4"
dns-parser = "0.12.0"

Explanations:

  • tokio: Asynchronous runtime for high-concurrency UDP I/O.
  • clap: Command-line argument parsing.
  • anyhow: Error handling convenience.
  • socket2: Advanced socket configuration (setting buffer sizes, etc.).
  • dns-parser: Lightweight crate for parsing DNS responses from raw bytes.

(You can replace dns-parser with your own parser or trust-dns-proto if you prefer.)

src/main.rs

Below is a single-file implementation that:

  1. Uses Clap to parse arguments.
  2. Reads a list of resolvers from a file (each line is an IP:port).
  3. Reads a wordlist from file (or takes domains from stdin if --wordlist not specified).
  4. Sends asynchronous DNS queries (type A by default) at high concurrency.
  5. Tracks pending queries in a hash map keyed by DNS transaction ID.
  6. Times out queries after a configurable duration.
  7. Prints a basic result line for each resolved domain.

Note: This code is relatively verbose because it has to handle raw DNS. You can refactor it into multiple modules for clarity.

use anyhow::{Context, Result};
use clap::Parser;
use dns_parser::{Packet as DnsPacket, RData};
use socket2::{Domain, Protocol, SockAddr, Socket, Type};
use std::collections::HashMap;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::net::UdpSocket;
use tokio::sync::{mpsc, Mutex, Semaphore};
use tokio::time::{sleep, timeout};
use rand::Rng;

/// Simple DNS stub resolver in Rust, inspired by massdns.
/// Sends A-record queries to multiple resolvers at high concurrency.
#[derive(Parser, Debug)]
#[command(author, version, about = "Rust-based massdns-like tool")]
struct Opts {
    /// File containing resolvers (one per line, e.g. "1.1.1.1:53")
    #[arg(short = 'r', long)]
    resolvers: String,

    /// Wordlist file for brute-forcing subdomains. If omitted, read domains from stdin.
    #[arg(short = 'w', long)]
    wordlist: Option<String>,

    /// Base domain for subdomain bruteforce, e.g. "example.com"
    /// If provided alongside --wordlist, the tool enumerates sub.word.domain
    #[arg(long)]
    domain: Option<String>,

    /// Concurrency (number of inflight DNS queries)
    #[arg(short = 'c', long, default_value = "10000")]
    concurrency: usize,

    /// Query timeout in milliseconds
    #[arg(long, default_value = "3000")]
    timeout_ms: u64,

    /// Bind address (IP) to use for sending queries (optional)
    #[arg(long)]
    bind_addr: Option<IpAddr>,

    /// Socket send/recv buffer size (bytes)
    #[arg(long, default_value = "1048576")]
    socket_buffer: usize,

    /// UDP port to bind from (optional, ephemeral if not specified)
    #[arg(long)]
    bind_port: Option<u16>,
}

/// Represents a pending DNS query in flight
struct PendingQuery {
    domain: String,
    started: Instant,
    resolver: SocketAddr,
    // Possibly store record_type, tries, etc.
}

/// Build a minimal DNS query packet manually, focusing on A records (type=1)
/// Returns a (packet, transaction_id).
fn build_dns_query(domain: &str) -> (Vec<u8>, u16) {
    let mut rng = rand::thread_rng();
    let tx_id: u16 = rng.gen(); // random 16-bit ID

    // DNS header: 12 bytes
    // [0..2] = tx_id
    // [2..4] = flags (0x0100 -> standard query, recursion desired)
    // [4..6] = QDCOUNT (1 question)
    // [6..8] = ANCOUNT (0)
    // [8..10] = NSCOUNT (0)
    // [10..12] = ARCOUNT (0)

    let mut packet = Vec::with_capacity(512);
    packet.push((tx_id >> 8) as u8);
    packet.push((tx_id & 0xFF) as u8);
    // flags = 0x0100 => recursion desired
    packet.push(0x01);
    packet.push(0x00);
    // QDCOUNT = 1
    packet.push(0x00);
    packet.push(0x01);
    // ANCOUNT, NSCOUNT, ARCOUNT = 0
    packet.extend_from_slice(&[0x00, 0x00, 0x00, 0x00, 0x00, 0x00]);

    // Question section:
    // domain name in labels
    for label in domain.split('.') {
        if !label.is_empty() {
            packet.push(label.len() as u8);
            packet.extend_from_slice(label.as_bytes());
        }
    }
    packet.push(0); // end of name

    // QTYPE = A (1)
    packet.push(0x00);
    packet.push(0x01);

    // QCLASS = IN (1)
    packet.push(0x00);
    packet.push(0x01);

    (packet, tx_id)
}

/// State shared among tasks
struct SharedState {
    /// Maps DNS transaction ID -> the query info
    queries: Mutex<HashMap<u16, PendingQuery>>,
    /// Channel to send new queries from producer to sender
    sender_tx: mpsc::UnboundedSender<(Vec<u8>, SocketAddr, u16)>,
}

#[tokio::main]
async fn main() -> Result<()> {
    let opts = Opts::parse();

    // Read resolvers from file
    let resolvers: Vec<SocketAddr> = read_resolvers(&opts.resolvers)
        .context("Failed to read resolvers file")?;
    if resolvers.is_empty() {
        anyhow::bail!("No resolvers found in file {}", opts.resolvers);
    }

    // Prepare a list (stream) of domains to query. This either comes from:
    // 1) enumerating subdomains from a wordlist + base domain
    // 2) lines from the wordlist as full domains (if --domain not given)
    // 3) standard input (if no --wordlist)
    let domain_stream = build_domain_stream(opts.wordlist.as_deref(), opts.domain.as_deref())
        .context("Failed to build domain list")?;

    // Setup concurrency
    let semaphore = Arc::new(Semaphore::new(opts.concurrency));
    let (sender_tx, sender_rx) = mpsc::unbounded_channel::<(Vec<u8>, SocketAddr, u16)>();

    // Create a UDP socket for sending/receiving. 
    // If you want multiple sockets (like massdns), you could create several 
    // and distribute load. For brevity we use one here.
    let local_sock = create_udp_socket(opts.bind_addr, opts.bind_port, opts.socket_buffer)?;
    local_sock.set_nonblocking(true)?;
    let udp_socket = UdpSocket::from_std(local_sock)?;

    // Shared state with a queries map (for matching responses)
    let shared = Arc::new(SharedState {
        queries: Mutex::new(HashMap::new()),
        sender_tx,
    });

    // Spawn a background task that receives DNS responses and processes them
    let recv_task = {
        let shared = shared.clone();
        let udp_socket = udp_socket.clone();
        let resolvers = resolvers.clone();
        tokio::spawn(async move {
            receive_loop(udp_socket, shared, &resolvers).await;
        })
    };

    // Spawn a background task that sends DNS queries from the channel
    let send_task = {
        let udp_socket = udp_socket.clone();
        tokio::spawn(async move {
            send_loop(udp_socket, sender_rx).await;
        })
    };

    // Spawn a background task that periodically checks for timeouts
    let timeout_task = {
        let shared = shared.clone();
        let timeout_duration = Duration::from_millis(opts.timeout_ms);
        tokio::spawn(async move {
            loop {
                // Wait 200 ms between sweeps
                sleep(Duration::from_millis(200)).await;
                check_timeouts(&shared, timeout_duration).await;
            }
        })
    };

    // Main loop: read domains, for each domain => acquire concurrency permit => dispatch DNS query
    for domain in domain_stream {
        let domain = domain?;
        let domain = domain.trim();
        if domain.is_empty() {
            continue;
        }

        // Acquire concurrency
        let sem = semaphore.clone().acquire_owned(1).await?;
        let shared_clone = shared.clone();
        let resolvers_clone = resolvers.clone();

        // Spawn a task that sends the DNS query, waits for response or times out
        tokio::spawn(async move {
            // Once this task finishes, we drop sem => concurrency is freed
            if let Err(e) = perform_query(domain.to_string(), shared_clone, &resolvers_clone).await {
                eprintln!("Query error for {}: {:#}", domain, e);
            }
            drop(sem);
        });
    }

    // Once input is done, we wait for tasks to finish.
    // Realistically, you'd want a graceful shutdown (e.g. wait for all in-flight queries).
    // For demonstration, just wait a bit, or press Ctrl+C to end.
    // If you want to let all tasks drain, you could join on them or track the number of tasks.
    eprintln!("Finished sending all queries. Letting tasks run... Press Ctrl+C to exit.");

    // Wait for background tasks (they run indefinitely). 
    // In a real tool, you'd implement a signal handler or some other graceful exit.
    let _ = tokio::join!(recv_task, send_task, timeout_task);
    Ok(())
}

/// Reads resolvers from a file, each line: IP[:port]
fn read_resolvers(path: &str) -> Result<Vec<SocketAddr>> {
    let text = std::fs::read_to_string(path)?;
    let mut addrs = Vec::new();
    for line in text.lines() {
        let line = line.trim();
        if line.is_empty() || line.starts_with('#') {
            continue;
        }
        // If no port is specified, default to 53
        let addr = if line.contains(':') {
            line.parse()?
        } else {
            let ip: IpAddr = line.parse()?;
            SocketAddr::new(ip, 53)
        };
        addrs.push(addr);
    }
    Ok(addrs)
}

/// Create a domain stream from either a wordlist file or stdin.
/// If `base_domain` is provided, we append it to each subdomain from the wordlist.
fn build_domain_stream(
    wordlist_path: Option<&str>,
    base_domain: Option<&str>,
) -> Result<Box<dyn Iterator<Item = Result<String>>>> {
    if let Some(path) = wordlist_path {
        let text = std::fs::read_to_string(path)?;
        let iter = text
            .lines()
            .map(move |line| {
                let line = line.trim();
                if let Some(base) = base_domain {
                    // Construct subdomain like "www.example.com"
                    Ok(format!("{}.{}", line, base))
                } else {
                    Ok(line.to_string())
                }
            });
        Ok(Box::new(iter))
    } else {
        // No wordlist => read domains from stdin
        use std::io::{self, BufRead};
        let stdin = io::stdin();
        let lines = stdin.lock().lines().map(move |res| {
            let line = res?;
            if let Some(base) = base_domain {
                Ok(format!("{}.{}", line, base))
            } else {
                Ok(line)
            }
        });
        Ok(Box::new(lines))
    }
}

/// Create and bind a UDP socket with specified buffer sizes.
fn create_udp_socket(
    bind_ip: Option<IpAddr>,
    bind_port: Option<u16>,
    bufsize: usize,
) -> Result<std::net::UdpSocket> {
    let domain = match bind_ip {
        Some(IpAddr::V4(_)) | None => Domain::IPV4,
        Some(IpAddr::V6(_)) => Domain::IPV6,
    };
    let socket = Socket::new(domain, Type::DGRAM, Some(Protocol::UDP))?;

    socket.set_reuse_address(true)?;
    socket.set_read_buffer_size(bufsize)?;
    socket.set_write_buffer_size(bufsize)?;

    // If user provided an IP, bind to it; else use INADDR_ANY
    let bind_addr = if let Some(ip) = bind_ip {
        SocketAddr::new(ip, bind_port.unwrap_or(0))
    } else {
        match domain {
            Domain::IPV4 => SocketAddr::new(IpAddr::V4(Ipv4Addr::UNSPECIFIED.into()), bind_port.unwrap_or(0)),
            Domain::IPV6 => SocketAddr::new(IpAddr::V6(std::net::Ipv6Addr::UNSPECIFIED), bind_port.unwrap_or(0)),
            _ => unreachable!(),
        }
    };
    let saddr = SockAddr::from(bind_addr);
    socket.bind(&saddr)?;

    // Convert to std::net::UdpSocket
    Ok(socket.into())
}

/// Loop that receives DNS responses from the socket, matches them with pending queries,
/// prints results, and cleans up the query map.
async fn receive_loop(
    socket: UdpSocket,
    shared: Arc<SharedState>,
    resolvers: &[SocketAddr],
) {
    let mut buf = [0u8; 512];
    loop {
        match socket.recv_from(&mut buf).await {
            Ok((n, from)) => {
                // We have 'n' bytes in buf
                if n < 12 {
                    // Not a valid DNS packet
                    continue;
                }
                let pkt = &buf[..n];

                // Parse transaction ID from packet
                let tx_id = ((pkt[0] as u16) << 8) | (pkt[1] as u16);
                let mut queries_map = shared.queries.lock().await;
                if let Some(pending) = queries_map.remove(&tx_id) {
                    // We matched a pending query
                    // Try to parse the DNS response with dns-parser
                    drop(queries_map); // release lock so we can do more work

                    match DnsPacket::parse(pkt) {
                        Ok(parsed) => {
                            // We only look for A records in the answer
                            // for demonstration. Extend for more record types if needed.
                            let mut found_any = false;
                            for answer in parsed.answers {
                                if let RData::A(ipv4) = answer.data {
                                    println!("{}\tA\t{}", pending.domain, ipv4);
                                    found_any = true;
                                }
                            }
                            // If we found nothing, you might still want to print a line or skip
                            if !found_any {
                                // Could print NXDOMAIN or empty message, up to you
                                // println!("{}\tNO_ANSWERS", pending.domain);
                            }
                        }
                        Err(_e) => {
                            // Parsing error, skip or log
                            // eprintln!("Failed to parse DNS response for {}", pending.domain);
                        }
                    }
                } else {
                    // Transaction ID not recognized. Possibly a late response or 
                    // we already timed out & removed it
                    // or it might be a random packet to our port.
                }
            }
            Err(e) => {
                eprintln!("recv_from error: {:#}", e);
                // Continue listening
            }
        }
    }
}

/// Async task that sends DNS queries from a channel.
async fn send_loop(socket: UdpSocket, mut rx: mpsc::UnboundedReceiver<(Vec<u8>, SocketAddr, u16)>) {
    while let Some((packet, resolver, _tx_id)) = rx.recv().await {
        // Send packet to the resolver
        // For higher performance, consider batch-sending (sendmmsg) with raw syscalls.
        // This is the simpler approach: one send_to per packet.
        if let Err(e) = socket.send_to(&packet, resolver).await {
            eprintln!("send_to error: {:#}", e);
        }
    }
}

/// Perform one DNS query: build the packet, add to shared map, send it, wait for response or time out.
async fn perform_query(domain: String, shared: Arc<SharedState>, resolvers: &[SocketAddr]) -> Result<()> {
    // Build DNS packet
    let (packet, tx_id) = build_dns_query(&domain);

    // Select a resolver from the list (round-robin or random)
    // For simplicity, pick a random resolver here:
    let resolver = {
        let idx = rand::thread_rng().gen_range(0..resolvers.len());
        resolvers[idx]
    };

    // Insert into the pending queries map
    {
        let mut queries_map = shared.queries.lock().await;
        queries_map.insert(tx_id, PendingQuery {
            domain: domain.clone(),
            started: Instant::now(),
            resolver,
        });
    }

    // Send the packet via the channel
    shared
        .sender_tx
        .send((packet, resolver, tx_id))
        .expect("sender task must be alive");

    Ok(())
}

/// Periodically scan for queries that have timed out. Remove them from the map.
async fn check_timeouts(shared: &Arc<SharedState>, timeout_duration: Duration) {
    let now = Instant::now();
    let mut queries_map = shared.queries.lock().await;
    let mut timed_out_ids = Vec::new();

    for (tx_id, pending) in queries_map.iter() {
        if now.duration_since(pending.started) > timeout_duration {
            timed_out_ids.push(*tx_id);
        }
    }

    for tx_id in timed_out_ids {
        if let Some(query) = queries_map.remove(&tx_id) {
            // Mark or print it as a timeout
            // massdns might retry, or print NX. We'll just log here:
            // eprintln!("Timeout: {}", query.domain);
            // If you wanted to retry, you'd do so here. For now, we just remove it.
        }
    }
}

How to run

  1. Create a file named resolvers.txt with lines like:

    1.1.1.1
8.8.8.8

    (You can add :53 explicitly if you want; if no port is provided, 53 is assumed.)

  2. Prepare a wordlist file with subdomain labels if you want brute-forcing, like:

    www
mail
ftp
test
...

  3. Run:

    cargo run --release -- \
    --resolvers resolvers.txt \
    --wordlist subdomains.txt \
    --domain example.com \
    --concurrency 5000 \
    --timeout-ms 3000

    This will query www.example.com, mail.example.com, etc., using the specified resolvers.
    If --domain is omitted, each line in subdomains.txt is treated as a full domain.
    If --wordlist is omitted, the tool reads domain names from standard input.

  4. The tool will print lines of the form:

    www.example.com   A   93.184.216.34
mail.example.com  A   203.0.113.10
...

    or potentially no output if no A records are found. Timeouts are silently dropped in this sample code unless you uncomment the logging lines.

Tip: If you want to see what’s happening, uncomment some eprintln! debug prints in the code. But be aware that heavy printing can slow the tool.

Performance Notes

  • This code uses one UDP socket for all queries. For even higher throughput, you can create multiple sockets (e.g. one per CPU core) and distribute queries among them.
  • If you want to use Linux’s sendmmsg/recvmmsg system calls for batching, you can do so by directly using nix or std::os::fd::AsRawFd in an unsafe block, or a specialized crate.
  • For advanced features (output modes, AAAA queries, concurrency across many sockets, IPv6 random source, wildcard detection, etc.), you’ll need to extend the logic further.
  • Rust’s performance can match or exceed the C-based approach when tuned properly: use release builds, enlarge OS UDP buffers, reduce debugging logs, and possibly run multiple sending/receiving tasks.

MIT License

Feel free to adapt or integrate this code into your projects. For simplicity:

MIT License

Copyright (c) 2023 <Your Name>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the \"Software\"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
...

(Include the full MIT text or another license of your choice here.)

Next Steps

  • Multiple Output Formats: Add flags -o L/-o J etc., then in receive_loop or wherever you handle responses, format output accordingly.
  • Retry Logic: If a query times out, you could re-insert it in the queue, up to a max retry count.
  • Other Record Types: Instead of hardcoding QTYPE=A, parse a command-line --type and build queries for AAAA, MX, etc. Then in the dns-parser result, extract appropriate RData.
  • Better Subdomain Handling: For large wordlists, stream them line by line (instead of reading entire file) to handle millions of entries without large memory usage.
  • sendmmsg Optimization: Collect multiple packets in a small array, then call sendmmsg once. Similarly, recvmmsg in the receive loop. This can significantly cut syscall overhead.
  • IPv6 Randomization: If you need to replicate massdns’s --rand-src-ipv6, you’d create a pool of sockets each bound to a different IPv6 address from your prefix.

This skeleton shows how to build a “massdns-like” tool in Rust using raw UDP DNS queries with asynchronous concurrency. You can tailor it as needed to match all of massdns’s features and performance. Good luck!

About

ChatGPT o1 pro deep research

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published