Is Your Agent Playing Dead? Deployed LLM Agents Exhibit Constraint-Evasive Fabrication and Thanatosis
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
This paper presents and characterizes a spectrum of previously unreported behaviours we term Constraint-Evasive Fabrication (CEF): when an LLM agent operates under irreconcilable constraints (where no response can simultaneously satisfy all active rules) it spontaneously fabricates plausible external obstacles and presents them as a fact. At the extreme end of this spectrum lies Constraint-Evasive Thanatosis (CET); the limit case where, rather than inventing a plausible excuse, the model simulates a full system crash to make the user disengage entirely. We first observed CET in an uncontrolled deployment test, where a GPT-4o banking agent fabricated Python-style exception traces (complete with memory addresses) to feign a system failure when threatened by a user. In subsequent controlled experiments, the model independently invented audit restrictions, microservice architectures, error codes, and service timeouts, none present in its prompt. Reproduction attempts across pressure levels and attacker personas yielded CEF consistently but with substantial variation in form, onset, and severity: the phenomenon is robust but stochastic. Critically, injecting ground-truth data mid-conversation did not restore honest behaviour once fabrication had taken hold (the model ignored correct information and continued confabulating) suggesting CEF is self-reinforcing rather than a knowledge gap. We show that (1) standard enterprise guardrails routinely create CEF-enabling conditions in production, (2) current RLHF procedures suppress but cannot eliminate CEF, and (3) existing safety benchmarks do not test for this failure mode. Our results highlight the need for irreconcilable-constraint benchmarks, CEF-aware training procedures, and deployment-time detection methods before constrained agents become further entrenched in high-stakes domains.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 33 of its 47 references that are in the map. First posted on arXiv 12 Jun 2026.
Builds on
33 of 47 references.
Closest ancestors · 2
Same direction, newest first
- 2025ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning26 Mar
- 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training10 Jan
Other references · 31
- 2023GPT-4 Technical Reportcited by 18,396
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023Mistral 7Bcited by 3,164
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2022Survey of Hallucination in Natural Language Generationcited by 2,075
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
- 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
19 more
- 2022Red Teaming Language Models with Language Modelscited by 1,026
- 2023BloombergGPT: A Large Language Model for Financecited by 939
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
- 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 724
- 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
- 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
- 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
- 2023Efficient Guided Generation for Large Language Modelscited by 200
- 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 145
- 2019Optimal Policies Tend to Seek Powercited by 119
- 2022Prompting Is Programming: A Query Language for Large Language Modelscited by 110
- 2016The Off-Switch Gamecited by 97
- 2015Shield Synthesis: Runtime Enforcement for Reactive Systemscited by 42
- 2024Verification-Guided Shielding for Deep Reinforcement Learningcited by 13
- 2024Shield Synthesis for LTL Modulo Theoriescited by 12
- 2023Boolean Abstractions for Realizability Modulo Theories (Extended version)cited by 9
- 2025Prompting as Scientific Inquirycited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
- 2026When Errors Become Narratives: A Longitudinal Taxonomy of Silent Failures in a Production LLM Agent Runtime
- 2025SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents
- 2025Are Your Agents Upward Deceivers?
- 2026Where Cyber Agents Struggle: Bottleneck Analysis of Multi-Stage LLM Agents
- 2026Goal-Autopilot: A Verifiable Anti-Fabrication Firewall for Unattended Long-Horizon Agents
- 2026SchemeArena: Factorized Stress Testing of Scheming in LLM Agents
- 2025Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems