Why You Shouldn't Use LLMs to Write Bug Bounty Reports (And the Ethical Way to Report Vulnerabilities)

For independent security researchers, the promise of generative AI is incredibly alluring. You find a niche edge-case vulnerability—perhaps a subtle race condition or a missing rate-limit on an obscure API endpoint—and instead of spending an hour drafting reproduction steps, you feed your raw terminal logs into Gemini 1.5 Pro or Claude and ask it to write a polished, professional security report.

On paper, it is a massive timesaver. In reality, security triagers on platforms like HackerOne and Bugcrowd are currently drowning in a tidal wave of useless, AI-generated noise.

Using LLMs to completely write and submit bug bounty reports is quickly becoming the fastest way to get your researcher account banned. It is damaging the relationship between the security community and engineering teams, and frankly, it is ticking off the very triagers who hold the keys to your payouts.

Here is why automated security reporting is a disaster, and how to use LLMs ethically without sacrificing your reputation.

The Plague of Hallucinated Severity

To understand why security teams despise LLM-generated reports, you have to understand the typical output of an untrained model. LLMs are optimized for helpfulness and eloquence. When you give them a minor, low-risk bug, their default behavior is to make it sound as exciting and significant as possible.

This leads to several systemic issues in automated reports:

1. The "Missing Header" Melodrama: A classic example is a report generated because a website is missing the X-Content-Type-Options: nosniff header. A human researcher knows this is a low-severity, best-practice issue. An LLM, eager to please, will draft a three-page report explaining how this omission could theoretically allow a highly sophisticated attacker to execute a cross-site scripting (XSS) attack and completely compromise the enterprise database. It turns a minor configuration note into a fictional critical emergency.
2. Confidently Wrong CVSS Scores: LLMs love assigning Common Vulnerability Scoring System (CVSS) scores. Unfortunately, they are terrible at calculating them. They frequently hallucinate metrics, claiming that a vulnerability requires "Low Privileges" when it actually requires full admin access, just to push the score into the "High" or "Critical" range.
3. Fictional Exploit Code: Ask an LLM to write a proof-of-concept (PoC) script for a vulnerability, and there is a high probability it will generate Python or Bash code that syntactically looks perfect but uses non-existent library methods or fails to execute entirely.

When a triage team receives fifty of these bloated, over-dramatised, and ultimately non-exploitable reports a day, they stop treating researchers as partners. They start treating them as spammers.

The Human Cost of AI-Generated Noise

Security triage is a deeply human bottleneck. Real engineers have to read your report, spin up a test environment, attempt to replicate your steps, and determine if a patch is required.

When you submit a report written entirely by an LLM, you are essentially passing the cognitive load of filtering out AI hallucinations onto a human engineer who is already overworked. This isn’t just inefficient; it is ethically questionable. It abuses the open-ended trust of responsible disclosure programs.

If you want to see how this dynamic plays out from the platform side, check out our Gemini Articles Hub for discussions on how models handle raw technical data ingest, and why they struggle with context-aware severity.

How to Use LLMs Responsibly in Security Research

This does not mean you have to abandon LLMs entirely. They are incredibly powerful tools for security researchers when used as cognitive assistants rather than ghostwriters.

Here is how to design an ethical, AI-assisted reporting workflow:

1. Write the Reproduction Steps Yourself The core of any good bug report is the reproduction steps. Do not let an LLM write these. If you cannot explain, in your own words, step-by-step how to trigger the vulnerability using a standard terminal command or browser action, you do not understand the bug well enough to report it.

2. Use LLMs to Clean, Not Create If English is not your primary language, or if you struggle with formatting markdown, LLMs are brilliant. Write a messy, bullet-pointed draft of your findings with your raw HTTP requests, and feed it to the model with a highly constrained prompt:

> "I have written the following draft of a security vulnerability report. Please correct any grammatical errors and format it cleanly using Markdown. Do not add any new technical details, do not estimate the security impact, and do not attempt to write a Proof of Concept script. Stick strictly to the facts provided."

You can find more examples of structuring safety-focused prompts in our Prompt Generator.

3. Verify Every Single Claim If the LLM suggests that your bug might lead to a specific type of escalation, do not take its word for it. Treat the LLM’s output as a hypothesis that *you* must verify manually. If you cannot personally prove that the escalation is possible, leave it out of the report.

Restoring Trust in Responsible Disclosure

The security industry relies on a fragile compact of mutual respect between independent researchers and defense teams. LLM-generated spam is actively eroding that trust, forcing companies to close their public programs or restrict submissions to invited testers.

By keeping your reports human, concise, and rigorously verified, you stand out from the noise. You get paid faster, you build a lasting reputation, and you help keep the web secure without making life miserable for the engineers on the other side of the screen.