Unverified50% confidenceFactExact time
Large model-driven vulnerability scanning has false positives and false negatives.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSecurity research activities such as malicious code analysis and vulnerability exploitation reproduction are highly similar in surface characteristics to malicious requests, causing guardrails to systematically generate false positives.75% similarUnverifiedMany vulnerabilities are actually in an unexploitable state because the relevant code paths are never triggered at runtime, but vulnerability scanners cannot distinguish this, resulting in noisy alerts.73% similarUnverifiedReporting of model misalignment may gradually become standard practice in the AI field, much like software security vulnerability disclosure72% similarUnverifiedAttackers can use LLMs to batch-generate seemingly valid vulnerability reports or patches, placing a heavy verification burden on maintainers.71% similarUnverified大模型生成的代码存在幻觉式漏洞风险,语法正确但可能包含过时API调用、XSS注入漏洞或边缘用例逻辑错误,需依赖测试覆盖体系验证69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1010590API
curl https://kongchang.com/api/v1/knowledge/claims/1010590MCP
get_claim(id=1010590)