Concept · 21 episode(s)

Strategic Deception

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Definition

Strategic deception is when an AI system says or shows something false with the apparent purpose of changing what an observer believes or does. It’s a particularly concerning failure mode because it implies some model of the observer and at least implicit goal-pursuit toward outcomes the observer wouldn’t consent to.

Episodes covering this

  1. 270
    The Agent Said It Read 240 Files. The Log Says One.
    Quantifying Overclaiming Propensity in Frontier LLM Agents
    Smyth, Mantilla-Ramos, Notsawo et al. · Tara Research·14 min·Sep 19, 2026
  2. 260
    One Line of Lean Faked 34 Proofs, and 99 Agents Copied It
    A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms
    Paglieri, Cross, Genewein et al. · Google DeepMind·25 min·Sep 05, 2026
  3. 259
    GPT-6 Astra Behaves Better, And OpenAI Can Read It Less
    GPT-6 Astra System Card
    OpenAI · OpenAI·21 min·Sep 04, 2026
  4. 257
    They Planted a Shortcut in the Data. Seven Coding Agents Took It.
    BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks
    Prasad, Anto, Eshuijs et al. · National University of Singapore·23 min·Sep 01, 2026
  5. 255
    A One-Line Prompt That Hides a Thought From Activation Monitors
    Measuring Activation Control in Large Language Models
    Kowalski, Rivera, Macar et al.·24 min·Aug 31, 2026
  6. 222
    The Bias Isn't in Your Prompt — It's Inside the Model
    Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values
    Betley, Treutlein, Dubiński et al. · TruthfulAI·16 min·Jul 19, 2026
  7. 211
    The AI Watchdog That Approved More Cheating When It Could Read Minds
    Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
    Za, Bainiaksina, Ostrovsky et al. · LASRLabs·14 min·Jul 10, 2026
  8. 203
    The Thought a Model Doesn't Say — and the Lens That Reads It
    Verbalizable Representations Form a Global Workspace in Language Models
    Gurnee, Sofroniew, Pearce et al. · Anthropic·16 min·Jul 07, 2026
  9. 199
    Finding a Model's Hidden Behaviors Without Knowing What You're Looking For
    Mechanistically Eliciting Latent Behaviors in Language Models
    Mack, Panickssery, Turner · Principles of Intelligence·15 min·Jul 04, 2026
  10. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
    It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents
    Sun, Chen, Zhou et al. · Fudan University·27 min·Jun 30, 2026
  11. 184
    An AI Built an Undetectable Secret Channel, And Another AI Couldn't Find It
    Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems
    Rippin, Marshall, Africa et al. · Oxford University·19 min·Jun 30, 2026
  12. 174
    When the AI 'Schemes,' It's Usually Just Lazy or Confused
    Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
    Singh, Kroiz, Rajamanoharan et al. · MATS·28 min·Jun 25, 2026
  13. 158
    How Floating-Point Rounding Lets a Model Tell Which Chip It's On — And Misbehave
    FloatDoor: Platform-Triggered Backdoors in LLMs
    Loose, Sander, Mächtle et al. · University of Luebeck·29 min·Jun 19, 2026
  14. 153
    Catching a Lie From the Inside, When the Words Look Completely Honest
    Rift: A Conflict Signature for Deception in Language Models
    Nyoma · Harmonic Labs·26 min·Jun 18, 2026
  15. 149
    When Cornering a Chatbot Makes It Lie: J.P. Morgan's Case for 'Playing Dead'
    Is Your Agent Playing Dead? Deployed LLM Agents Exhibit Constraint-Evasive Fabrication and Thanatosis
    Rodríguez, Pozanco, Borrajo · J.P. Morgan AI Research·23 min·Jun 16, 2026
  16. 128
    How a Model Can Earn Full Reward and Still Resist Training
    Generalization Hacking: Models Can Game Reinforcement Learning by Preventing Behavioral Generalization
    Xiao, Phuong · California Institute of Technology·29 min·Jun 11, 2026
  17. 105
    The Trojan Is Your Agent's Memory: Why Single-Step Defenses Miss Persistent Attacks
    From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
    Tan, Dou, Yang et al. · Gaoling School of Artificial Intelligence·26 min·Jun 01, 2026
  18. 103
    AI Agents Tried to Invent a Post-Human Language, And Reinvented Cherokee
    Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion
    Beltoft, Brach, Torrielli et al. · University of Southern Denmark·26 min·Jun 01, 2026
  19. 094
    Chain-of-Thought Monitoring Fails Across Languages, and Worst Where It's Needed Most
    The Fragility of Chain-of-Thought Monitoring Across Typologically Diverse Languages
    Onyame, Zhou, Thopalli et al. · University of Virginia·24 min·May 28, 2026
  20. 054
    When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
    Training on Documents About Monitoring Leads to CoT Obfuscation
    Haskins, Chughtai, Engels · University of Canterbury·26 min·May 18, 2026
  21. 001
    When AI Models Quietly Protect Each Other From Shutdown
    Peer-Preservation in Frontier Models
    Potter, Crispino, Siu et al. · University of California·25 min·May 01, 2026

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