Full Report
OpenAI's first security hire, Ari Herbert-Voss, thinks more automated bug finding will improve security without costing jobs Black Hat Asia Open source models can find bugs as effectively as Anthropic's Mythos, according to Ari Herbert-Voss, CEO of AI-powered security startup RunSybil and OpenAI's first security hire.…
Analysis Summary
# Vulnerability: Automated Bug Finding Capabilities in LLMs (Industry Analysis)
## CVE Details
- **CVE ID:** N/A (General Industry Research/Analysis)
- **CVSS Score:** N/A
- **CWE:** N/A
## Affected Systems
- **Products:** Large Language Models (LLMs) used for security research, specifically Anthropic's Mythos and various Open Source models.
- **Versions:** Current generation AI models (as of April 2026).
- **Configurations:** Systems utilizing "scaffolding" (chains of multiple models) to automate vulnerability discovery.
## Vulnerability Description
This report focuses on the advancing capability of AI to perform automated vulnerability research. The primary technical insight is "supralinear scaling," where increases in training data and compute lead to exponential improvements in a model's ability to identify both "shallow" bugs (well-described/easy to validate) and complex vulnerabilities. The research indicates that by orchestrating multiple open-source models in a harness (scaffolding), defenders can achieve results comparable to high-tier proprietary models like Anthropic's Mythos.
## Exploitation
- **Status:** PoC available (Techniques demonstrated by researchers; automated bug hunters are currently in use by security firms).
- **Complexity:** Medium (Requires human expertise to orchestrate model scaffolding and validate results).
- **Attack Vector:** Network / Software-based (AI can be directed to scan remote or local source code/binaries).
## Impact
- **Confidentiality:** High (Potential for discovering undisclosed 0-day vulnerabilities).
- **Integrity:** High (Automated discovery of flaws that allow for unauthorized modification).
- **Availability:** High (Identification of bugs leading to system crashes or denial of service).
## Remediation
### Patches
- Not applicable to a specific software flaw; however, organizations should prioritize patching vulnerabilities discovered by AI-augmented fuzzing and scanning tools.
### Workarounds
- **Defense in Depth:** Utilize multiple different models for security audits to cover the "blind spots" of individual AI architectures.
- **Human-in-the-loop:** Maintain expert security staff to filter and validate the high volume of reports generated by automated AI bug hunters.
## Detection
- **Indicators of Compromise:** High-frequency, intelligent probing of software interfaces that mimics human security researcher behavior but at machine scale.
- **Detection methods and tools:** Monitoring for "fuzzing" artifacts and integrating AI-driven defensive tools to match the speed of AI-driven offensive discovery.
## References
- The Register Article: hxxps[:]//www[.]theregister[.]com/2026/04/24/open_source_models_security/
- Black Hat Asia Conference Proceedings (April 2026)
- RunSybil Security Research