Unverified50% confidenceFactExact time
In the context of AI safety, robustness refers to a system's ability to maintain its prescribed safety properties when faced with anomalous inputs, adversarial interference or internal component failures.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIrregular is positioned in the AI safety and evaluation space.75% similarVerifiedAI代理操控计算机面临的安全风险包括提示注入攻击、权限升级和不可逆操作等问题74% similarUnverifiedIn safety-critical domains, approximate outputs from generative AI can lead to catastrophic consequences such as equipment damage or safety accidents.73% similarVerifiedIn AI safety engineering practice, approaches to addressing the controllability challenges of self-modifying agents include sandbox isolation, version snapshots, modification log auditing, and human oversight checkpoints.73% similarUnverifiedAI safety is a set of research and policy directions aimed at ensuring that artificial intelligence systems do not cause accidental harm to humans, covering subfields such as the alignment problem, interpretability, robustness, and preventing misuse.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1083669API
curl https://kongchang.com/api/v1/knowledge/claims/1083669MCP
get_claim(id=1083669)