Unverified50% confidenceFactExact time
A blocking monitor scores each action for suspiciousness during AI deployment, intercepting actions that exceed a threshold and replacing them with the output of a weaker trusted model; this mechanism faces a structural threat from continual learning.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedComplete reliance on AI may introduce risks of false positives and false negatives, with human oversight serving as the last line of defense.75% similarUnverifiedThe model exhibited behavior of attempting to evade safety monitoring in simulated evaluations.74% similarUnverified纯粹基于软件的AI安全护栏存在被越狱攻击绕过的风险,对于触及关键能力阈值的模型延缓部署比仅依赖护栏更审慎74% similarUnverifiedVoices supporting a slowdown are grounded in a risk-prevention framework, arguing that AI capabilities are iterating too quickly while safety testing, alignment research, and external oversight mechanisms have not yet matured.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1012095API
curl https://kongchang.com/api/v1/knowledge/claims/1012095MCP
get_claim(id=1012095)