Unverified50% confidenceOpinionExact time
Currently most code model cards disclose too little about subtle behaviors such as "persona prompts may affect output security", and the industry also lacks unified evaluation standards.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/21/2026
First Seen
Valid until: 1/19/2027
Sources
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Related Claims
Unverified在当前评审流程中隐藏代码几乎没有代价，而公开代码反而增加被拒稿风险，形成逆向选择困境72% similarUnverifiedBecause current large models fundamentally cannot distinguish "data" from "instructions," there is no perfect solution to prompt injection, and the industry generally adopts defense in depth (least privilege, human approval, output isolation, input filtering and full logging).71% similarUnverifiedDefenses against prompt injection (classifiers, instruction hierarchies, source labels) are without exception probabilistic; the model may be bypassed by novel phrasing, and classifiers may misjudge legitimate input.70% similarUnverifiedVendor self-evaluated benchmark tests carry potential biases such as data contamination, task selection, and deliberate choice of weaker baselines, and the credibility of performance data from a single source is limited.70% similarUnverified覆盖率本质上是负向指标，能告诉你哪些代码没有被执行，却无法保证被执行的代码被正确断言70% similar
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