Chiosa
Research collection

AI Security & Safety

Research on AI Security & Safety: explanations, evidence, limitations and connections between papers.

37 results

  1. Match each safety claim to the evidence that survives the handoff

    A repainted image may still resemble its source after an invisible ownership mark weakens. A coding agent may likewise carry a plausible summary or file forward after losing the origin of a request. The result alone leaves a gap in what a later check can establish. Across the studied settings, evidence has to travel with the claim it supports. Speaker and artifact history matter for later actions; component records matter for runtime identity; build inputs matter for exact reproduction. Each study tests a different link in that chain.

  2. Match an automated pass to the failure paths it tested

    A faster training recipe, a warning from a code checker, and a patch that blocks an exploit can each receive an encouraging automated result. The practical tension is whether that result covers the failure that matters for the next decision. Across these studies, a pass becomes useful when its test case and target are clear. Their checks screen settings, filter generated programs, or replay attacks, yet each leaves paths outside what it exercised. Read the pass as evidence for those tested paths.