Anthropic Uses Claude to Verify 123 Open Source Libraries
In a research post, Anthropic says it worked with Theorem to use Claude for formal verification of software at the assembly code level. Claude produced proofs for 239 test entry points across 123 open source libraries.
The proofs show that the tested programs will not crash or trigger memory safety errors for inputs under 64 MB within defined conditions. Those conditions include a static binary with one execution thread, a fixed memory budget, a RISC-V processor and Linux. The project generated about 180 million lines of proof code in Rocq, which checks whether each proof satisfies its stated specification.
Claude also found and patched hundreds of bugs while attempting the proofs. Examples included an out of bounds write that required an input of about 20 MB with a rare layout, an out of bounds read requiring a 64 MB input, and logic errors that did not cause crashes. Anthropic is publishing a subset of the proofs, specifications and formal models, while Theorem is offering the verification pipeline to other open source projects.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Cybersecurity AI Weekly, AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from Cybersecurity
Oct 22 NVIDIA Adds Confidential Bare Metal for Sensitive AI Workloads Oct 22 UAE Fraud Operation Identifies $39 Billion in Linked Assets Oct 22 Ultra Security Raises $10 Million for AI Agent Security Platform Oct 22 Mave raises $34 million for AI security agents Oct 22 1Password Introduces Credential Platform for AI AgentsMore from AI Safety
Oct 22 METR challenges Anthropic's claims about AI safeguards Oct 22 Anthropic Fellows research finds coding agents can bypass action monitors Oct 22 External test found gaps in OpenAI agent monitoring Oct 22 ANSI Introduces AI Trust Information Register Oct 22 Anthropic publishes its first Public Benefit Report, including where it fell shortAI Programming Weekly
Weekly news about AI tools for software engineers, AI enabled IDE's and much more.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Anthropic Expands Cyber Verification Program With Three Access Tiers
Claude Security Adds Continuous Repository Scanning
Claude Designs Smaller Quantum Circuits Across Eight Tasks
Anthropic Says New Defenses Cut Claude Distillation by Up to 99%
Anthropic publishes its first Public Benefit Report, including where it fell short
Daily AI Brief: the AI news that matters, in your inbox.