In a startling development that underscores the rapidly evolving landscape of cybersecurity, an AI tool named Sybil, developed by the startup RunSybil, recently uncovered a critical vulnerability that even its human creators hadn’t anticipated. This incident, involving a a customer’s federated GraphQL deployment, revealed a sophisticated flaw exposing confidential information – a discovery so profound that RunSybil cofounders Vlad Ionescu and Ariel Herbert-Voss found no public record of it anywhere online.
The AI’s Intuition: A “Step Change” in Discovery
Sybil, leveraging a blend of advanced AI models and proprietary techniques, is designed to meticulously scan computer systems for weaknesses. Its primary function is to identify potential exploits, from unpatched servers to misconfigured databases. However, the GraphQL discovery was different. It demanded an intricate understanding of multiple interconnected systems and their nuanced interactions, pushing the boundaries of what was thought possible for an AI.
“We scoured the internet, and it didn’t exist,” Herbert-Voss recounted. “Discovering it was a reasoning step in terms of models’ capabilities—a step change.” This wasn’t merely pattern recognition; it was a form of emergent intelligence, hinting at a new frontier in automated vulnerability detection.
An Inflection Point: The Accelerating Threat
This incident is not isolated but rather a potent indicator of a broader trend: as AI models grow in sophistication, so too does their capacity to unearth zero-day bugs and other critical vulnerabilities. The very intelligence that can safeguard systems can also be weaponized to exploit them.
Dawn Song, a distinguished computer scientist at UC Berkeley specializing in both AI and security, echoes this concern, describing the current moment as an “inflection point.” She highlights how recent advancements in AI, particularly “simulated reasoning” (breaking down complex problems) and “agentic AI” (models capable of web searches or running software), have dramatically amplified models’ cyber capabilities.
“The cyber security capabilities of frontier models have increased drastically in the last few months,” Song asserts, emphasizing the urgency of the situation.
Benchmarking AI’s Hacking Prowess
To quantify this burgeoning threat, Song co-created CyberGym, a benchmark designed to assess how effectively large language models (LLMs) can identify vulnerabilities in extensive open-source software projects. Comprising 1,507 known vulnerabilities across 188 projects, CyberGym offers a stark measure of AI’s rapid progress.
The results are compelling: Anthropic’s Claude Sonnet 4, tested in July 2025, could identify approximately 20 percent of the benchmark’s vulnerabilities. Just three months later, in October 2025, its successor, Claude Sonnet 4.5, boosted that figure to 30 percent. “AI agents are able to find zero-days, and at very low cost,” Song notes, signaling a dramatic shift in the economics and accessibility of cyber exploitation.
Forging a Shield: Countermeasures in the AI Era
The accelerating offensive capabilities of AI necessitate equally advanced defensive strategies. Song advocates for a multi-pronged approach, with AI itself playing a pivotal role in bolstering cybersecurity defenses.
Proactive Collaboration and Secure-by-Design
One proposed countermeasure involves frontier AI companies collaborating with security researchers, sharing models pre-launch. This allows experts to proactively identify and mitigate bugs before general release, transforming potential threats into opportunities for enhanced security.
Another transformative idea is to fundamentally rethink software development. Song’s lab has demonstrated the feasibility of using AI to generate inherently more secure code than what is typically produced by human programmers. “In the long run we think this secure-by-design approach will really help defenders,” Song explains, envisioning a future where vulnerabilities are engineered out from the outset.
The Looming Offensive Advantage
Despite these defensive innovations, the immediate future presents a significant challenge. The RunSybil team warns that AI’s rapidly advancing coding skills could grant hackers a formidable advantage. “AI can generate actions on a computer and generate code, and those are two things that hackers do,” Herbert-Voss cautions. “If those capabilities accelerate, that means offensive security actions will also accelerate.”
This emerging reality demands immediate attention and strategic investment in AI-driven defense mechanisms to prevent a potential imbalance in the digital battleground. The “inflection point” is here, and how we respond will define the future of cybersecurity.
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