arXiv Machine Learning

Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment

arXiv:2606. 26071v1 Announce Type: new Abstract: A central goal of safety research is determining whether a model is misaligned.

arXiv AI

Jul 7

Retroactive Chain-of-Thought (RetroCoT): Forensic Reconstruction Prompts as a Safety Diagnostic Across Model Generations

arXiv:2607. 04645v1 Announce Type: cross Abstract: Safety alignment in large language models is typically evaluated against direct, imperative harmful requests.

By Samira Hajizadeh

llms robotics multimodal safety

arXiv AI

Aug 5

Your Agentic LLMs Secretly Encode Latent Signals of Indirect Prompt-Injection Exposure

arXiv:2608. 02657v1 Announce Type: cross Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.

By Jianshuo Dong, Yiming Liu, Maosen Zhang, Nan Deng, Xu Peng, Xiaoping Zhang, Tianwei Zhang, Jie Zhang, Han Qiu

llms agents

arXiv AI

Jun 30

Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives

arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.

By Mohammed Abu Baker, Luca Baroni, Dan Wilhelm

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 30

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

arXiv:2607. 26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools.

By Ruoyu Wang, Heng Zhao, Renjie Wu, Mengnan Zhao, Zhixuan Chu, Wanyu Lin, Tianhang Zheng

llms agents

arXiv AI

5d ago

Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales

arXiv:2608. 13250v1 Announce Type: cross Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge.

By Long Hoang Nguyen, Brice Valentin Kok-Shun, Guangyu Du, Ali Sunyaev

llms fine-tuning safety

arXiv AI

Jun 10

When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models

arXiv:2606. 10740v1 Announce Type: new Abstract: Failures in multi-turn reasoning models are largely invisible to terminal-score evaluation.

By Sai Kartheek Reddy Kasu, Nils Lukas, Samuele Poppi

safety

arXiv AI

1d ago

Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments

arXiv:2608. 16747v1 Announce Type: cross Abstract: Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors.

By Adam Karvonen, Euan Ong, Subhash Kantamneni, Samuel Marks

llms agents safety

arXiv AI

6d ago

Agent Safety Should Be a Runtime Contract

arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.

By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang

llms agents reinforcement-learning safety

arXiv AI

Jun 10

PreAct-Bench: Benchmarking Predictive Monitoring in LLMs

arXiv:2606. 09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective.

By Hainiu Xu, Italo Luis da Silva, Jiangnan Ye, Yuhao Wang, Wei Liu, Linyi Yang, Jonathan Richard Schwarz, Nicola Paoletti, Yulan He, Hanqi Yan

llms agents benchmarks safety

arXiv Machine Learning

Jun 5

Trust, but Don't Verify: Epistemic Blind Spots in LLM Source Evaluation

arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.

By Rohan N. Pradhan, Steve Goley

llms safety

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv AI

6d ago

Backdoor Decontamination Dynamics in LLM Agents

arXiv:2608. 11295v1 Announce Type: cross Abstract: Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are never met during testing.

By Gabriel Huang, Abhay Puri, L\'eo Boisvert, Alexandre Drouin, Perouz Taslakian, Spandana Gella, Christopher Pal

llms agents fine-tuning

Related stories