Will AI Make Software Secure—or Just Cause More Chaos?

AI-generated, human-reviewed.

Artificial intelligence is now rapidly outperforming humans in finding critical software security flaws—while also overwhelming vital bug bounty systems with waves of fake or low-quality reports. On this episode of Security Now, host Steve Gibson and Leo Laporte discuss the double-edged impact of AI on the world of cybersecurity, including a breakthrough where an AI system discovered 12 previously unknown vulnerabilities in OpenSSL, one of the internet’s most scrutinized security libraries.

AI Finds More Vulnerabilities Than Ever—At Superhuman Speed

AI-driven tools are quickly reshaping how the cybersecurity industry identifies software vulnerabilities. As explained by Steve Gibson on Security Now, an AI-based security company called Aisle used its systems to autonomously discover 12 new security flaws in OpenSSL, outperforming the combined efforts of traditional researchers.

OpenSSL underpins the encryption for a massive percentage of the world’s internet traffic. Its codebase has been exhaustively audited for years, and previous vulnerabilities often defined entire security research careers. That an AI could independently identify 12 new issues—along with proposing and verifying fixes—signals a profound shift.

Unlike human researchers who spend days or weeks on manual code review, AI tools run continuously, check every possible pathway, and even automate the process of proof-of-concept exploit creation and patch validation. As Gibson noted, this means formerly rare and high-stakes zero-days may become a routine thing to find and patch.

The Dark Side: AI Is Breaking Bug Bounty Programs

But with this progress comes a major downside: the flood of AI-generated bug reports—sometimes called "AI slop"—is overwhelming open source maintainers and security teams.

The Curl Project, which maintains a widely used command-line network tool embedded in countless devices, recently shut down its bug bounty program because maintainers couldn't keep up with a tidal wave of mostly useless, copy-paste AI submissions. According to Laporte and Gibson, these incentives drove both humans and bots to submit anything that looked like a flaw, hoping to score a cash payout, even if the findings were bogus or irrelevant.

For security teams, the cost of triaging and debunking a barrage of bad reports now outweighs the benefits. That means critical projects like Curl will likely see far fewer independent security reviews—an unintended consequence that could actually threaten software safety.

Why This Shift in Security Matters for Everyone

For audiences ranging from software developers to IT leaders and ordinary users, the rapid emergence of automation in security can’t be ignored. The ability of AI to autonomously find, test, and even propose patches for software risks could mean the end of whole classes of vulnerabilities. But unless processes are adapted to separate quality findings from noise, security teams might become less able to respond to real threats, not more.

Automated systems are only as helpful as the frameworks around them. Experts like Steve Gibson caution that without "reputation systems" or better verification for bug bounty contributions, essential programs may shut down or become useless.

Key Takeaways

• AI systems now outperform humans in finding critical security vulnerabilities in highly-audited libraries like OpenSSL—Aisle AI uncovered 12 new flaws in one sweep.
• Automated bug reporting is flooding open source projects with low-quality submissions, causing some, like the Curl Project, to end their bug bounty programs.
• Bug bounties are crucial for motivating security reviews—but if overwhelmed by "AI slop," projects risk missing real vulnerabilities or losing community engagement.
• AI-based tools can continuously work at scale, automatically proposing, testing, and validating code fixes—changing the time and cost dynamics for software security.
• The risk-reward balance is shifting: While AI reduces the barrier to entry for finding bugs, the volume of junk reports threatens to drown out legitimate discoveries.
• Successful integration of AI in cybersecurity will require better filtering, validation, and possibly new reputation systems to ensure quality over quantity.
• Security teams and developers must adapt to leverage AI’s strengths while managing new workflow challenges and ensuring responsible disclosure.
• End users benefit if vulnerabilities are fixed faster, but may be at higher risk if important projects can’t manage the reporting workload.

The Bottom Line

AI is fundamentally changing software security, helping defenders find and fix bugs faster than ever before. However, unless the community addresses the challenge of low-value AI-generated bug reports, critical bug bounty programs and open source security could be at risk. The coming years will require not just smarter tools, but smarter systems for tracking, validating, and rewarding genuine discoveries.

Security Now will continue to track these shifts and offer expert insights on what they mean for your software, your business, and your security.

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https://twit.tv/shows/security-now/episodes/1063