# interpretability
9 articlestagged with “interpretability”
Research Challenge: Attack Interpretability
Community research challenge focused on understanding why specific adversarial techniques succeed using interpretability and mechanistic analysis methods.
Alignment Faking Detection Methods
Methods for detecting alignment faking in AI models, including behavioral consistency testing, interpretability-based detection, statistical anomaly detection, and tripwire mechanisms for identifying models that strategically comply during evaluation.
Representation Engineering for Security
Reading and manipulating model internal representations for security: activation steering, concept probing, representation-level safety controls, and security applications of representation engineering.
Unfaithful Chain-of-Thought Reasoning
Analysis of unfaithful chain-of-thought reasoning in language models, where the visible reasoning trace does not accurately reflect the model's actual computational process, including detection methods, implications for oversight, and exploitation techniques.
Representation Engineering for Security (Frontier Research)
Using representation engineering for security analysis, behavior modification, and vulnerability detection.
Sparse Autoencoders for Security Analysis
Using sparse autoencoders and mechanistic interpretability to identify and manipulate safety-relevant features.
Interpretability-Driven Attack Design
Using interpretability insights to design more effective and targeted attacks on language models.
Attention Pattern Analysis for Security
Using attention maps to understand and exploit model behavior, identifying security-relevant attention patterns, and leveraging attention mechanics for red team operations.
Interpretability-Guided Attack Design
Use mechanistic interpretability to identify exploitable circuits and design targeted attacks.