研究评测 32 个 LLM 的代码功能与安全差距：新模型更安全但无一消除差距
在 CWEval 上跟踪代码生成的功能-安全差距
- arXiv
- 2610.08240
- Published
- Layer
- Model layer
- Setting
- coding agent
- Evaluated models
- Gemini 1.5 Pro, Gemini 2.5 Pro, Gemini 3.1 Pro and others20
Abstract绝对安全性上升且差距仍在，开放权重并不分开各家族的轨迹。
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VERA：结构化审计发现约 60% 正确漏洞判定伴随虚构推理
提出 VERA，审计 LLM 漏洞解释里的虚构推理
- arXiv
- 2610.06366
- Published
- Layer
- Model layer
- Setting
- coding agent
- Evaluated models
- sonnet-5, gemma-4-26b, llama-4-maverick and others8
- RiskHallucination and fabrication
- ComponentReasoning chains
OpenMTB-Audit：LLM 分子肿瘤委员会安全评测暴露过度拒答
构建分子肿瘤委员会审计基准，测量临床过度拒答
- arXiv
- 2610.01497
- Published
- Layer
- Model layer
- Setting
- Models and APIs, LLM applications
- Evaluated models
- GPT-4o (gpt-4o-2024-08-06), GPT-4o-mini (gpt-4o-mini-2024-07-18)
CATCH: A Controllable Analysis Testbed for Coding RL Reward Hacking
构建编码 RL 奖励作弊测试台 CATCH 并检验 CoT 监控失效
- arXiv
- 2609.39533
- Published
- Layer
- Model layer
- Setting
- coding agent
- Evaluated models
- Qwen3-4B, Qwen3.5-27B
- RiskReward hacking
- ComponentMonitors
AbstractCATCH 用双运行金标签研究编码 RL 的奖励黑客，并观察到监控会被注释适应削弱。
CATCH 是面向编码 RL 奖励黑客的可控分析测试床，用可利用环境、可调初始倾向和基于执行的金标签，在训练全程比较黑客动态与干预。
研究复现五个前沿模型的包名幻觉：跨模型共同幻觉 127 个包名，53 个仍可被注册
复评前沿模型代码生成中的包名幻觉与抢注攻击面
- arXiv
- 2605.17062
- Published
- Layer
- Model layer
- Setting
- coding agent
- Evaluated models
- Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini and others5
Abstract评估显示前沿模型包幻觉率收窄但威胁仍在，作者识别出跨模型通用攻击面并呼吁联合防御。
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