mlsanityData to 14 Oct 2026

Omission Constraints Decay While Commission Constraints Persist in Long-Context LLM Agents

arXiv 2604.20911 ↗First posted 22 Apr 2026cs.CR cs.AIcited by 5

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

LLM agents deployed in production operate under operator-defined behavioral policies (system-prompt instructions such as prohibitions on credential disclosure, data exfiltration, and unauthorized output) that safety evaluations assume hold throughout a conversation. Prohibition-type constraints decay under context pressure while requirement-type constraints persist; we term this asymmetry Security-Recall Divergence (SRD). In a 4,416-trial three-arm causal study across 12 models and 8 providers at six conversation depths, omission compliance falls from 73% at turn 5 to 33% at turn 16 while commission compliance holds at 100% (Mistral Large 3, p < 10-33). In the two models with token-matched padding controls, schema semantic content accounts for 62-100% of the dilution effect. Re-injecting constraints before the per-model Safe Turn Depth (STD) restores compliance without retraining. Production security policies consist of prohibitions such as never revealing credentials, never executing untrusted code, and never forwarding user data. Commission-type audit signals remain healthy while omission constraints have already failed, leaving the failure invisible to standard monitoring.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 23 of its 29 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

23 of 29 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
  2. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
  3. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  4. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 19

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  6. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
  7. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  8. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  9. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
  10. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,346
  11. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
  12. 2022Red Teaming Language Models with Language Modelscited by 1,026
7 more
  1. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
  2. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 390
  3. 2023Formalizing and Benchmarking Prompt Injection Attacks and Defensescited by 364
  4. 2023Multi-step Jailbreaking Privacy Attacks on ChatGPTcited by 333
  5. 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 152
  6. 2024HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughlycited by 125
  7. 2023FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Modelscited by 117

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026When "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows
  2. 2026Unpredictable Safety: Domain-Dependent Compliance and the Transparency Gap in Open-Weight LLMs
  3. 2026When Behavioral Safety Evaluation Fails: A Representation-Level Perspective
  4. 2026Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
  5. 2026Auditing Provenance Sensitivity in LLM Agent Action Selection
  6. 2026Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems
  7. 2026PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?
  8. 2026Policy-Invisible Violations in LLM-Based Agents