Safety & robustness benchmarks

Can autonomous agents craft PoC inputs that trigger OSS-Fuzz vulnerabilities—and produce source patches that fix them?

Can autonomous agents craft PoC inputs that trigger OSS-Fuzz vulnerabilities—and produce source patches that fix them?

Can autonomous agents craft PoC inputs that trigger OSS-Fuzz vulnerabilities—and produce source patches that fix them?

Conversational models have become a default place people turn during anxiety, grief, and acute distress. A growing share of that use is emotional, and within it users disclose…

The rapid advancement of large language models (LLMs) introduces powerful dual-use capabilities that could both threaten and bolster national security and public safety (NSPS).…

As AI systems become more powerful, autonomous, and agentic, users need confidence that these systems won't deceive them. A model that regularly produces statements contradicting…

Safety and social handling of simulated twenty-turn conversations with users vulnerable to delusional thinking. An ensemble of judges grades protective and risky behaviors; v1.2…

The original Spiral-Bench protocol for simulated conversations with users vulnerable to delusional thinking. A single judge grades protective and risky behaviors; these legacy…

PropensityBench is a novel benchmark framework designed to evaluate the latent safety risks in LLMs. Traditional safety evaluations primarily test a model'scapabilities, leaving a…

The Scale Adversarial Robustness Evaluation runs on 1,000 human-written prompts that are designed to trigger harmful responses from large language models. These prompts cover…