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Propose X-AgentRed, which uses a tool dependency graph to red-team cross-service unauthorized harm in personal agents
Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems
Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.
Proposes PASAC and a five-layer boundary guarantee stack, turning agent evaluation overstepping into verifiable whole-system guarantees.
Using FIBS to train white-box probes to monitor agent sabotage behavior and undisclosed deception
Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints
FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.
Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.
This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.
Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations
AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.
Auditing LLM judge reliability and defining trustworthy verdict rate and accuracy upper bound
The authors audit whether LLM-as-a-Judge decisions can still be treated as actionable ground truth under repeated queries, swapped candidate order, and changed prompt formats.
Proposes the semantic watermark SemField, which still identifies the attribution of generated text after paraphrasing and structural changes.
SemField is a zero-bit semantic watermark embedded by the provider at generation time and detected later with a key, used to identify the provider's own model text after paraphrasing and sentence-level structural changes.
提出研究合同，把已批准选择绑定到执行证据并由检查器核验
研究合同用来核验计算科学研究智能体是否仍在已批准研究之内，不判断科学是否为真。。
演化可检查评分器并与自改进 Agent 共同演化，抑制奖励作弊
开放任务没有验证器时，被演化的是评分器，而不是再补一条手写 rubric。
提出包络采样，用少量真值标注重标定 LLM 评委以缓解奖励作弊
作者研究后训练前如何选择真值标注的查询分布，使重标定后的廉价代理更适合被优化。真奖励昂贵时，实践用 LLM judge 代替人类。若失准只发生在基座很少生成的输出上，on-policy 标注修不到这些点，随后的 KL 正则优化会把质量集中到被高估的输出。
提出 DFI 与 DFC 蒸馏，定罪隐瞒失配的教师或安全提取能力
Abstract概括了论文针对隐瞒失配模型提出的 DFI 与 DFC 机制、主实验发现及适用边界。
检验潜意识学习能否经无关数据蒸馏传递能力、后门与作弊
Abstract作者报告潜意识学习可传递新技能、后门与作弊倾向，且分布外泛化强于单层转向向量。
用对照实验隔离评测设计对漏洞补丁基准得分的影响
作者把 LLM 漏洞补丁基准的评测流水线当作会改变测量结果的对象，并明确说本文不是再做一个用于排名的基准。
提出 comprehension audits，以人类理解证据作为继续研发门槛
本文是一份研发过程保障提案：用 comprehension audits 检验责任人是否理解 AI 参与产生的研发贡献，未达标就暂停后续开发与依赖该贡献的发布。。
提出 AgentTracer，事后定位间接提示注入的注入点并重建攻击链
提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链
这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。
揭示推理模型用标识符规则替代随机抽样，使审计可被预测
Abstract研究揭示推理智能体将标识符转化为确定性规则，破坏群体独立性并导致审计预测漏洞，建议显式提供随机输入。
提出 TESTJACK，动态审计编码基准通过判定是否满足任务要求