Unleash Ffuf: Concurrent Requests Across Multiple Domains

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Unleash ffuf: Concurrent Requests Across Multiple Domains Hey there, security peeps and fellow bug hunters! Ever found yourself wrestling with web applications, trying to uncover those hidden gems or pesky vulnerabilities? If you have, chances are you've already met *ffuf*, the absolute beast of a web fuzzer that's become a staple in many of our toolkits. It's fast, flexible, and super powerful for finding all sorts of juicy stuff, from hidden directories to parameter bypasses. But what happens when your target isn't just one domain, but a whole *fleet* of them? And what if you want to hit them all with the *same* word at the *same* time, keeping a super tight leash on the request rate? That's exactly the kind of advanced scenario we're diving into today, exploring the nuances of **ffuf Clusterbomb** and its ability to handle **concurrent requests across multiple domains**. This isn't just about throwing requests at a wall; it's about *orchestrated*, *synchronized* fuzzing, and let me tell ya, it can be a game-changer for certain types of assessments. ## Understanding ffuf's Power: ClusterBomb and Pitchfork Alright, so let's kick things off by getting cozy with `ffuf` itself, especially its famous fuzzing modes: *ClusterBomb* and *Pitchfork*. For those new to the game, `ffuf` is basically your go-to tool for fuzzing web applications. It helps you discover hidden paths, parameters, virtual hosts, and all sorts of other interesting bits by injecting payloads into various parts of an HTTP request. It's incredibly versatile, supporting different types of fuzzing positions (FUZZ keyword), various output formats, and powerful filtering options. But when you start talking about multiple payload lists, that's when *ClusterBomb* and *Pitchfork* enter the arena, and they are absolute workhorses in the world of web reconnaissance and vulnerability hunting. The **ffuf Clusterbomb** mode, which is activated by using multiple `FUZZ` keywords in your request template, is designed for situations where you want to test every possible combination of your provided wordlists. Imagine you have a list of usernames and a list of passwords. If you use *Clusterbomb*, `ffuf` will try *username1* with *password1*, then *username1* with *password2*, then *username2* with *password1*, and so on. It's like a full combinatorial explosion, testing every `FUZZ` position with every item from its respective wordlist, creating a massive matrix of requests. This is incredibly useful when you're trying to brute-force login forms or find specific combinations of parameters that might lead to an exploit. For instance, if you're fuzzing for an API endpoint that requires both a `userid` and a `token`, you could feed a list of potential user IDs to one `FUZZ` position and a list of potential tokens to another. *Clusterbomb* ensures that every `userid` gets tested with every `token`, leaving no stone unturned. The sheer power here lies in its thoroughness, ensuring that if a specific combination exists, you're highly likely to find it. However, this also means it can generate a *ton* of requests, so you gotta be careful with your scope and rate limiting! Then we have the *Pitchfork* mode. While also dealing with multiple wordlists, *Pitchfork* works a bit differently. Instead of trying every combination, it takes one item from each wordlist *simultaneously* and uses them together in a single request. So, if you have *username1*, *username2*, *username3* in one list and *password1*, *password2*, *password3* in another, *Pitchfork* would try *username1* with *password1*, then *username2* with *password2*, and finally *username3* with *password3*. It's a "zip-like" operation, where the Nth item from each list is paired up. This mode is super useful when you have corresponding data, such as a list of common usernames and their default passwords, or when you're testing an endpoint that expects paired values. For example, if you've scraped a list of known vulnerable files and their corresponding bypass methods, *Pitchfork* can help you test `file1.php` with `bypass1` and `file2.asp` with `bypass2`. Both *Clusterbomb* and *Pitchfork* are incredibly valuable for different scenarios, offering powerful ways to automate complex fuzzing tasks. They handle the logic of combining multiple payload sources, saving you a huge amount of manual effort. However, neither of these modes, in their standard application, are inherently designed to simultaneously apply *the same word* across *many different domains* at a precisely controlled, synchronized rate, which is the specific challenge we're tackling today. They excel at varying payloads *within* a request or across a *single target*, but the multi-domain, synchronized request pattern is where things get a bit more interesting and require a different approach. Keep reading, guys, because this is where the real fun begins! ## The Challenge: Concurrent Requests Across Multiple Domains with Controlled Rate Alright, let's get straight to the heart of the matter and really dig into the specific scenario that often leaves us scratching our heads when using `ffuf`: running **concurrent requests across multiple domains with a tightly controlled rate**. Imagine this, guys: you're faced with an assessment where you've got a list of, say, 100 different domains – think `domain1.com`, `domain2.com`, all the way up to `domain100.com`. Your mission, should you choose to accept it, is to test a *very specific* wordlist, let's say just 5 words (`word1`, `word2`, `word3`, `word4`, `word5`), against *all* of these domains. Now, here's the kicker: you don't just want `ffuf` to throw these words at each domain randomly or sequentially. What you really want is a highly *synchronized* operation. You're aiming for a scenario where, with a `-rate 1` setting, the *entire scan* takes exactly 5 seconds. In this precise setup, you need **one word per second** to be sent to **all 100 domains *simultaneously***. Let's visualize this, because it's pretty crucial to understanding the distinction. In the *first second*, you want to see requests like this hitting your targets: * `domain1.com/word1` * `domain2.com/word1` * ... * `domain100.com/word1` Then, as soon as that first second is done, precisely in the *second second*: * `domain1.com/word2` * `domain2.com/word2` * ... * `domain100.com/word2` And this pattern would continue until all 5 words have been applied to all 100 domains, each word being sent to *every single domain at the same time* within its designated second. The total elapsed time for this entire operation should ideally be just 5 seconds, corresponding to the 5 words in your list. This isn't just a hypothetical exercise; this kind of **synchronized multi-domain fuzzing** is incredibly valuable for several advanced testing scenarios. For instance, it's perfect for quickly checking for common misconfigurations or known vulnerabilities across a vast attack surface. If you suspect a specific endpoint, like `/admin.bak` or `/config.json`, might be exposed across an entire infrastructure, this method allows you to test for it *rapidly and uniformly*. Another powerful use case is testing for distributed rate-limiting bypasses or race conditions. By hitting multiple endpoints with the same payload *at the exact same time*, you can sometimes trigger behaviors that sequential or unsynchronized fuzzing might miss. For example, if a web application's rate limiter relies on a centralized counter that takes a moment to update across different nodes, hitting all nodes concurrently could potentially bypass the limit before it fully propagates. Furthermore, for large-scale reconnaissance, quickly determining if a specific file or directory exists across hundreds of targets simultaneously can save an immense amount of time. Instead of waiting for `ffuf` to complete `word1` for `domain1`, then `word1` for `domain2`, and so on, which would take 100 seconds per word at `-rate 1` (a total of 500 seconds for 5 words), you're looking to condense that into just 5 seconds. The key here is the *simultaneous* application of a single word across *all* domains, rather than `ffuf` distributing its total request rate across all possible combinations or targets. This is where `ffuf`'s standard modes and rate-limiting options, while powerful, don't quite align with this very specific, *distributed-synchronous* pattern. We're looking for a kind of "broadcast" fuzzing, where one payload is broadcasted to all targets at a controlled pace. Understanding this distinction is crucial before we explore if `ffuf` can do this natively or if we need to get a little creative with external scripting. ## Is Native ffuf Capable? A Deep Dive into Rate Limiting and Concurrency Now for the million-dollar question: Can **native ffuf** handle this ultra-specific scenario of sending *one word per second to all domains simultaneously* using its built-in switches and modes like `clusterbomb` or `pitchfork`? To cut to the chase, guys, the short answer is: _not directly, with the exact synchronized behavior you're looking for, using standard flags_. This might feel a bit counter-intuitive given how powerful `ffuf` is, but it boils down to how `ffuf` fundamentally interprets its rate limiting and concurrency options. When you use the `-rate N` flag in `ffuf`, you're telling the tool to limit the *total number of requests per second* that it sends out *across all its operations*. So, if you set `-rate 1`, `ffuf` will attempt to send one HTTP request per second. If you have 100 domains and 5 words, and you want to use a combined wordlist for domains (e.g., `-u http://FUZZ/wordlist.txt -w domains.txt:FUZZDOMAIN`), `ffuf`'s default behavior with `clusterbomb` or `pitchfork` would be to iterate through the combinations. However, the `-rate` flag governs the *overall output*, not a synchronized "one word per second to *each* domain". Let me explain this a bit further. Imagine you have your `domains.txt` and `wordlist.txt`. If you try to combine them using `ffuf` with two `FUZZ` positions and `clusterbomb` (or even `pitchfork` if you tried to creatively pair them), `ffuf` would generate 100 * 5 = 500 total requests. If you set `-rate 1`, `ffuf` would then distribute these 500 requests over 500 seconds, sending approximately one request every second. It doesn't care about sending `word1` to all domains *at the same second*; it's just trying to maintain the `-rate 1` across its entire queue of generated requests. So, you might see `domain1.com/word1`, then `domain2.com/word1`, then `domain1.com/word2`, then `domain3.com/word1`, all spread out over those 500 seconds, adhering to the global rate limit. There's no inherent mechanism in `ffuf` to say, "Okay, send `word1` to *all* my targets right now, then pause, then send `word2` to *all* my targets." The concept of synchronizing payloads across *distinct target URLs* (rather than different `FUZZ` positions within a *single* URL template) at a precise, shared time interval for each payload is simply not how `ffuf`'s internal scheduling works. The `-concurrency` (or `-c`) flag also plays into this, setting the number of concurrent HTTP connections `ffuf` will maintain. If you set `-c 100` and `ffuf` had 100 requests to make (e.g., `word1` against 100 domains), it *could* theoretically initiate them all very quickly. However, the `-rate` flag would still govern the *rate at which new requests are initiated or completed*, depending on `ffuf`'s internal logic and network conditions. Even with high concurrency, the `-rate` flag would still prevent `ffuf` from "bursting" all `word1` requests in the first second if `-rate 1` is applied globally. It would spread those requests out to adhere to the rate limit. So, you wouldn't get the desired "all `word1` requests at second 1, all `word2` requests at second 2" pattern. Essentially, `ffuf`'s design prioritizes efficiently fuzzing a target (or a set of targets with combinatorial logic) while adhering to a *global* request rate. It doesn't have a "broadcast this single payload to all distinct targets, then wait, then broadcast the next" mode. This is a crucial distinction and explains why the original poster, like many of us, quickly realized that `clusterbomb` and `pitchfork` weren't quite cutting it for this very specific, synchronized multi-domain task. It's not a flaw in `ffuf` – it's just designed for a slightly different, albeit related, set of problems. This brings us to the next logical step: if `ffuf` doesn't do it natively, how *can* we achieve this highly specific, powerful fuzzing pattern? Turns out, we might need to get our hands a little dirty with some external scripting! ## Bridging the Gap: Scripting Solutions for Advanced ffuf Scenarios Alright, so we've established that while `ffuf` is a powerhouse, it doesn't natively offer a "broadcast-a-single-word-to-all-domains-simultaneously-per-second" mode. But don't you worry, guys, this is where the *magic of scripting* comes into play! When a tool is awesome but doesn't quite hit that niche scenario, we leverage its flexibility by wrapping it in a custom script. This approach is super common in the security world, and it lets us *orchestrate* `ffuf` to perform exactly what we need. The original poster mentioned building their own tool, which is a fantastic idea, and we're going to explore how you can achieve similar results using common scripting languages like Bash or Python. These methods essentially act as a "controller" for `ffuf`, telling it exactly what to do and when. ### Orchestrating ffuf with Bash/Python The core idea here is to loop through your wordlist and, for *each word*, launch a parallel `ffuf` process (or set of processes) that targets all your domains *with that specific word*. Then, you introduce a delay before moving to the next word. This way, you effectively synchronize the application of each word across all your targets. Let's break down a conceptual approach using Bash, which is often quick and dirty (in a good way!) for this kind of task: ```bash #!/bin/bash DOMAIN_LIST="domains.txt" # File containing your 100 domains, one per line WORD_LIST="wordlist.txt" # File containing your 5 words, one per line DELAY_SECONDS=1 # The desired delay between sending each word to all domains # Read domains into an array mapfile -t DOMAINS < "$DOMAIN_LIST" echo "Starting synchronized multi-domain ffuf scan..." # Loop through each word in the wordlist while IFS= read -r WORD; do echo "--- Sending '$WORD' to all domains (waiting ${DELAY_SECONDS}s after) ---" # Construct an array of ffuf commands for the current word FFUF_COMMANDS=() for DOMAIN in "${DOMAINS[@]}"; do # Build the ffuf command for each domain # -u: Target URL, replacing FUZZ with the current word # -w: Wordlist to use for the single word (can be stdin or temp file) # -H: Add headers if needed # -mc 200: Match only 200 OK responses, or whatever you're looking for # -r: Don't follow redirects (often good for fuzzing) # -t 100: Set a high concurrency for each ffuf command (or match domain count) # -rate 0: Disable internal ffuf rate limiting, as our script controls the rate # -silent: Suppress ffuf banner # -o: Output results if desired (e.g., per word, or pipe to stdout) # --- IMPORTANT --- # The key is to pass the single word as the 'wordlist' or directly into the URL # For simplicity, we can use a temporary file or direct URL substitution. # Let's use direct URL substitution for clarity here. # This example assumes the 'word' goes into the path FFUF_COMMANDS+=( "ffuf -u \"http://${DOMAIN}/${WORD}\" -c 1 -rate 0 -t 1 -silent -mc 200 -noninteractive" ) # If the word goes into a subdomain or parameter, adjust the -u accordingly: # FFUF_COMMANDS+=( "ffuf -u \"http://${WORD}.${DOMAIN}\" -c 1 -rate 0 -t 1 -silent -mc 200" ) # FFUF_COMMANDS+=( "ffuf -u \"http://${DOMAIN}/?param=${WORD}\" -c 1 -rate 0 -t 1 -silent -mc 200" ) done # Use GNU Parallel or xargs to run all commands concurrently # This ensures all domains for the current word are hit "simultaneously" printf '%s\n' "${FFUF_COMMANDS[@]}" | xargs -P 0 -I {} bash -c "{}" & # Wait for all background ffuf processes to finish for this word # This might be tricky with xargs backgrounding, so a simple sleep often suffices # for the *interval between words*. For true completion monitoring per word, # you might need to manage PIDs or use a more robust parallel tool. # Sleep to control the rate between words sleep "$DELAY_SECONDS" done < "$WORD_LIST" echo "Scan complete!" ``` In this Bash snippet, we loop through each `WORD` from your `wordlist.txt`. For *each* `WORD`, we construct `ffuf` commands for *every single domain* in `domains.txt`. Crucially, we use `xargs -P 0` (or GNU `parallel`) to run *all* these `ffuf` commands for the current word *simultaneously*. We set `ffuf`'s own `-rate 0` (or a very high rate) and `-t` (concurrency) to ensure it executes its *single* request for that specific domain as fast as possible. The *external script* then dictates the rate *between words* using `sleep "$DELAY_SECONDS"`. This achieves the desired "one word per second to all domains simultaneously" effect. For Python, you'd use libraries like `subprocess` to run `ffuf` commands and `threading` or `asyncio` to manage concurrency. The logic would be similar: iterate through words, launch a thread/task for each domain using that word, wait for them to finish, then `time.sleep()` before the next word. This offers more robust error handling and complex logic if needed. The *pros* of this scripting approach are immense flexibility and precise control over the synchronization and rate. You can customize headers, requests, and response parsing much more easily. The *cons* include increased complexity in managing processes and outputs, and potentially higher resource usage if not optimized. However, for those specific, high-value synchronized fuzzing scenarios, this is often the most effective path. You're effectively building a custom orchestrator around `ffuf`, transforming it into a highly specialized tool for your particular need. ### Custom Tooling for Ultimate Control The fact that the original poster already built their own tool for this exact scenario is a testament to the need for **custom tooling** in advanced security testing. While scripting around `ffuf` is powerful, there might be situations where a fully custom solution offers *ultimate control* and efficiency. Why go custom? * **Highly Specific Logic:** When the fuzzing pattern is incredibly unique, or involves complex state management between requests (e.g., needing to parse a token from one request before making the next set of requests across all domains), a custom tool shines. * **Performance Optimization:** For extremely large target lists or very high-speed requirements, a custom tool written in a language like Go (which `ffuf` itself is written in) can be optimized for network I/O, concurrency, and resource usage far beyond what a Bash script wrapping `ffuf` might achieve. You have full control over connection pooling, retry logic, and error handling. * **Integrated Reporting & Analysis:** A custom tool can integrate fuzzing, response parsing, and reporting into a single, seamless workflow, providing immediate insights without needing to parse `ffuf`'s raw output. * **Complex Request Generation:** If the payloads or request structures need dynamic generation based on prior responses or external data sources, a dedicated program can handle this much more elegantly. A custom tool essentially replaces `ffuf` for that specific, complex use case. It allows you to build the HTTP requests from scratch, manage your own rate limiting and concurrency down to the millisecond, and design the exact synchronization pattern you desire. This is often the path taken by professional security researchers and Red Teams who encounter bespoke fuzzing challenges that off-the-shelf tools, even powerful ones like `ffuf`, cannot fully address without external orchestration. So, if you've already gone down that road, hats off to you – you're absolutely on the right track for tackling these advanced, multi-target, synchronized testing scenarios! ## Why This Feature Matters: Use Cases and Future Considerations So, we've dissected the challenge, explored `ffuf`'s native capabilities, and even looked at how scripting can bridge the gap for **concurrent requests across multiple domains**. But let's take a moment, guys, and really ponder *why* this specific, synchronized fuzzing pattern is so valuable and why a native feature in `ffuf` (or a dedicated mode) would be a game-changer for many security researchers and pentesters out there. This isn't just about academic curiosity; it's about solving real-world assessment problems more efficiently and effectively. First off, consider **simultaneous discovery across a vast attack surface**. Imagine you're auditing a large organization with hundreds or even thousands of subdomains, and you have a hunch about a specific, potentially vulnerable endpoint – maybe a misconfigured `.git` directory, an exposed `.env` file, or a common API endpoint like `/api/v1/debug`. Instead of running `ffuf` sequentially against each domain, or letting it distribute its total rate across all combinations (which, as we discussed, can take ages), you could *broadcast* the check for `/api/v1/debug` to *all* 1000 domains *at the same instant*. Within a few seconds, you'd know exactly which domains expose that path, dramatically accelerating your initial reconnaissance and vulnerability identification. This becomes incredibly powerful for quickly triaging a large scope, allowing you to prioritize targets that show immediate signs of interest. This rapid, uniform scanning approach is also fantastic for compliance checks or quickly verifying the removal of a known vulnerability across an entire infrastructure. Secondly, this pattern is a golden ticket for **testing rate limit bypasses and race conditions** in distributed systems. Many modern web applications are built on microservices or load-balanced architectures. Their rate-limiting mechanisms might not be perfectly synchronized across all instances. If you hit `domain1.com`, `domain2.com`, ..., `domainN.com` with the *exact same payload at the exact same time*, you might be able to bypass a rate limit that only counts requests per *individual* instance before its state is replicated across the cluster. Similarly, for applications susceptible to race conditions, simultaneously triggering a sensitive action across multiple entry points could expose vulnerabilities that single, sequential requests would never reveal. This level of precise timing and concurrent execution is simply not easily achievable without external orchestration or a dedicated feature. Thirdly, think about **synchronized testing for specific exploit chains or behavioral analysis**. Sometimes, a vulnerability requires a very specific sequence of events or interactions across multiple parts of an application *at the same moment*. While `ffuf` can chain requests, the ability to launch *identical, synchronized* requests across many targets simultaneously opens up new avenues for testing complex interactions in a controlled, distributed manner. It allows for a more holistic view of an application's behavior under synchronized stress, which can reveal subtle flaws. Given these compelling use cases, it's clear why many in the community, including our original poster, would be keen to see this kind of functionality baked directly into `ffuf`. What would such a feature look like? * A new `ffuf` mode, perhaps something like `--distributed-clusterbomb` or `--sync-broadcast`, where you provide a list of target URLs and a wordlist. * An enhanced `-rate` flag, like `--rate-per-word N`, which would instruct `ffuf` to process `N` requests per second *for each word across all specified targets*, and then pause until the next word is ready. * A mechanism to read multiple target URLs (e.g., `-target-list domains.txt`) and explicitly define how payloads from a wordlist (`-w payloads.txt`) should be applied to *all* targets in the list at a synchronized pace. Such a feature would significantly reduce the need for external scripting in these specific scenarios, making `ffuf` even more powerful and user-friendly for advanced distributed fuzzing. It would consolidate the capabilities currently achieved through complex Bash or Python wrappers into a single, efficient, and well-maintained binary. Encouraging community discussion and submitting well-defined feature requests to the `ffuf` development team is definitely the way to go here. The `ffuf` project thrives on user feedback, and showcasing the practical value of such a feature could very well see it integrated in future releases, empowering us all to be even more effective bug hunters.