• Published: 11 November 2025 at 14:41 UTC

• Updated: 11 November 2025 at 14:41 UTC

If you've ever used Burp Intruder or Turbo Intruder, you'll be familiar with the ritual of manually digging through thousands of responses by repeatedly sorting the table via length, status code, etc. I've developed an algorithm called HTTP Anomaly Rank which can efficiently pick out the most interesting responses for you.

HTTP Anomaly Rank is now integrated into Turbo Intruder and automatically places the most anomalous findings at the top of the results table without you needing to lift a finger.

We've also integrated this algorithm into Burp Suite's API in release 2025.10, so you can easily use it in your own tools. Since it can efficiently sift through massive result sets, it's particularly useful for anyone who wants to build AI based features.

In this post I'll explain how the algorithm works in depth but first, here's a quick demo:

HTTP Anomaly Rank

HTTP Anomaly Rank evolved from the diffing logic I developed during my Backslash Powered Scanner: Automating Human Intuition research back in 2016. I built Backslash Powered Scanner to discover unknown injection vulnerability classes by recognising subtle differences in responses to payload pairs such as " vs ". Accurately diffing HTTP responses is a notoriously difficult problem as they're often very noisy, but I eventually found a reliable approach based on calculating a large number of response attributes (think status code, line count, exact byte sequence...), identifying which ones are stable, and using these for response comparison. This let me answer the question "Are the responses to these two payloads consistently different", and automate discovery of some really nice vulnerabilities. Check out the whitepaper and presentation for the full details.

HTTP Anomaly Rank scores every response based on how different it is from the others. First, it calculates a weight for every attribute based on how stable it is.

For example, consider the response set above.

• Status only has two unique values so it gets a high weighting.
• Content-Type never changes so it's useless
• CRC32 is unique on every response so it's useless (perhaps the username is reflected)
• Word-count has three unique values so it's useful but weighted lower than status

The algorithm then looks at every response, and assigns it a score based on how unique the attribute values seen on that response are, combined with how unique it is. In this example, ADMIN ends up with the highest score because it has a unique word-count.

This approach means it can detect and flag responses with tiny discrepancies, even when the overall response content is very noisy - check the video above for an example.

The score increases in accuracy as we collect more responses. The calculation is quite computationally heavy but the algorithmic complexity is O(N) so it scales well. In Turbo Intruder, I made the score only get calculated when the attack is completed to minimise wasted CPU cycles.

Turbo Intruder Integration

Part of my vision for Turbo Intruder is to require as few user clicks as possible. To help achieve this, it will now automatically sort the results table by the anomaly rank column when the attack completes. Hopefully this strategy works for you but if not, you can override it and automatically sort by any column of your choice using table.setSortOrder()

I'm looking forward to announcing some more quality of life updates for Turbo Intruder soon!

If you see the "Anomaly Rank" column but it's always set to 0, that means you need to update Burp Suite. This feature is available in 2025.10 and later.

Let me know how you find it, and if you'd like this feature in Burp Intruder too.

Enjoy!

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