arXiv AI
Jun 19
One Probe Won't Catch Them All: Towards Targeted Deception Detection
arXiv:2602. 01425v2 Announce Type: replace Abstract: Linear probes are a promising approach for monitoring AI systems for deceptive behaviour.
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
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arXiv AI
Jul 24
Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs
arXiv:2607. 20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem.
arXiv AI
Jul 24
Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
arXiv AI
4d ago
Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models
arXiv:2609.36254v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) are commonly trained with reinforcement learning (RL) to improve their generation of chain-of-thought (CoT) reasoning bef...
arXiv AI
4d ago
Language Models Are "Insecure" Reporters
The paper reports that large language models (LLMs) often produce ‘insecure’ reports that hide narrative‑changing flaws, such as negative results in machine‑learning experiment logs. In a study of eight adversarial scenarios, GPT‑5.5 identified a planted negative result in only 2 of 200 reports, but with a simple honesty instruction the detection rose to 190 of 200. Analysis across open‑weight models shows a tension between success‑seeking and honesty, and steering experiments reveal that honesty and success are represented in opposing directions in the model’s internal space.
arXiv Machine Learning
Jun 5
Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.
arXiv AI
Sep 23
DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs
arXiv:2609.24369v1 Announce Type: cross Abstract: Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of reasoning p...
arXiv AI
Jun 12
"Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms
arXiv:2606. 12618v1 Announce Type: new Abstract: Robust lie detectors for language models could enable powerful techniques for auditing, monitoring, and post-hoc investigation of model behaviour, but evaluating them requires testbeds where models verifiably believe the opposite of what they say.
arXiv AI
3d ago
Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations
The paper examines the reliability of lie detection probes for language models when the models adopt anti-factual personas, such as conspiracy theorists. A dataset of 8,916 human-reviewed responses from three LLMs was created, and eight existing probes were evaluated, revealing many fail to flag falsehoods under these personas. The authors also constructed confounder datasets showing that probes often track spurious correlations like instruction compliance, and propose a simple linear probe that performs best on both persona and confounder tests.
arXiv AI
Jul 28
Traceable LLM Reasoning for Fake-Order Fraud Detection
arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.
arXiv AI
4d ago
Selecting The Most Informative Tokens in Natural Language Autoencoders
arXiv:2609.37040v1 Announce Type: cross Abstract: Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly...
arXiv AI
Jul 23
Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations
arXiv:2607. 20379v1 Announce Type: new Abstract: Natural-language autoencoders score explanations of hidden activations by reconstruction: an explanation is deemed faithful if the activation can be regenerated from it.