AI-Generated Vulnerability Reports: The New Threat Undermining Bug Bounty Platforms

How a Fake Curl Report Uncovered a Growing Exploit in Cybersecurity Systems

In the ever-evolving world of cybersecurity, the latest threat doesn’t come in the form of malware, ransomware, or phishing—it’s language models. More specifically, automatically generated vulnerability reports crafted by AI are starting to infiltrate popular bug bounty programs, threatening to erode the very system designed to improve software security.

This was starkly illustrated in a recent incident involving the curl open-source project, which uncovered a fraudulent bug report submitted through the HackerOne platform. Although the report appeared technical and convincing at first glance, it was quickly dismissed upon deeper analysis. The problem? The vulnerability didn’t actually exist—it was pure AI-generated fiction.

This case, revealed by security researcher Harry Sintonen, spotlights a growing issue in cybersecurity: AI being misused to create plausible-sounding but fake vulnerability disclosures. These reports drain time and resources, deceive platforms, and exploit the trust that bug bounty programs are built upon.

The AI Scam That Tried to Fool curl: 30-Line Digest

A fraudulent bug report submitted to the open-source curl project via HackerOne has stirred serious concerns.
The report cited nonexistent functions, proposed baseless patches, and referred to fake commit hashes.
It was technically worded, seemingly credible, and written in a way that mimicked legitimate vulnerability disclosures.
The scam was flagged by researcher Harry Sintonen and quickly debunked by curl’s experienced maintainers.
Unlike many organizations, curl had the technical capacity and independence to catch the scam before any damage was done.
The report originated from the account @evilginx, which may have been used in similar scams targeting other platforms.
Many smaller or under-resourced projects could easily fall victim, paying out bounties without thorough vetting.
This incident highlights a new kind of exploit—not in software code, but in human processes and organizational workflows.
At platforms like Open Collective, developers are seeing an uptick in what they describe as “AI garbage” reports.
These submissions, though currently filterable, are becoming harder to distinguish from legitimate vulnerabilities.
Members of the Python Software Foundation security team echoed concerns about AI’s ability to waste expert review time.
These aren’t simple errors—they are intentionally fraudulent entries exploiting the financial incentives in bug bounty systems.
The more these scams spread, the more legitimate researchers are discouraged, and the credibility of disclosure systems suffers.
Some reports have even resulted in payments from organizations lacking deep in-house technical expertise.
HackerOne and similar platforms are being called out for failing to flag repeat offenders submitting AI-generated nonsense.
Suggestions for improvement include stronger identity verification, rigorous triage procedures, and AI-assisted screening tools.
Still, the ultimate responsibility falls on organizations to invest in expertise and critical analysis.
The curl case underscores the fragile trust that coordinated disclosure systems depend on.
If platforms and organizations don’t adapt, the entire vulnerability disclosure ecosystem could become unreliable.
This incident is a warning sign of how AI, while a powerful tool, can be twisted into a weapon of cyber fraud.
It’s no longer just about securing code—it’s about securing the processes and communities that protect that code.
AI misuse in cybersecurity highlights the dual-edged nature of technological innovation.
Many experts fear that the sustainability of open reporting systems could be compromised.
In a space where time is a premium and resources are limited, fraudulent noise can easily drown out genuine signals.
Organizations that don’t evolve their vetting procedures risk being gamed by increasingly convincing AI-generated submissions.
Meanwhile, skilled human researchers may be edged out of an industry that no longer rewards real talent and effort.
Bug bounty programs were meant to democratize security; now, they may need to be redefined to protect their integrity.
AI-assisted fraud isn’t just a technical problem—it’s a systemic challenge requiring policy, tools, and culture shifts.
From trust erosion to workflow disruption, the ripple effects of these fake reports are only beginning to surface.
The cybersecurity world must now prepare for a new era of fraud: where deception is synthetic, scalable, and deceptively smart.

What Undercode Say:

The recent revelation involving a fabricated bug report aimed at the curl project exposes deeper systemic weaknesses within modern bug bounty frameworks. What initially appears to be a simple case of AI misuse is, in fact, a symptom of an evolving cybersecurity threat landscape. This incident represents more than a single failed attempt—it’s a blueprint for how bad actors may increasingly weaponize language models to exploit under-resourced organizations.

What makes this threat particularly insidious is the plausible nature of AI-generated content. Language models like GPT can compose grammatically perfect, technically dense submissions that mimic real vulnerability disclosures. In a world where open-source maintainers are often overworked and underfunded, even sophisticated teams can fall for such content if they don’t have the time or tools to properly evaluate it.

Curl was lucky—or perhaps just prepared. Their maintainers had the technical acumen and confidence to call out the report for what it was: AI slop. But smaller organizations? They may not fare as well. Many teams are forced to rely on external triage from platforms like HackerOne, which—despite good intentions—can fail to filter these scams due to the sheer volume of submissions and limited vetting protocols.

What’s alarming is that these LLM-generated reports

Moreover, these incidents could create long-term consequences: organizations might become hesitant to run bounty programs at all, fearing legal or financial fallout from fraudulent claims. Others might implement draconian verification processes that dissuade ethical hackers from participating, swinging the pendulum too far the other way.

As this practice grows, we can expect even more elaborate AI fraud—possibly coordinated by malicious actors or commercial exploiters who use automation to scale their attacks. Without rigorous identity checks, multi-layer validation, and