Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Practitioners of prompt-composed agentic systems report a recurring failure mode: editing one prompt module silently shifts the behavior of others despite no shared variable or executable dependency. We formalize this as compositional behavioral leakage (CBL): interference between modules sharing a context window. CBL is enabled by architectural non-isolation: transformer self-attention provides no formal boundary between concatenated modules. We probe CBL on a deployed job-evaluation agent (Claude Sonnet 4.6, 144 trials) through a reusable three-channel protocol that perturbs non-focal modules along volume, content, and form. Only the content channel produces a detectable paired effect (Cohen's d = 0.63, bootstrap 95% CI excluding zero); no recommendation flipped -- a sub-threshold regime invisible to standard QA but compounding across the thousands of decisions a deployed agent makes. CBL is orthogonal to known agent-failure axes (adversarial injection, cognitive degradation, multi-agent fault propagation, privacy leakage). We contribute an operational definition, a reusable protocol, a falsifiable prediction set, and a system-class characterization, establishing cross-module interference measurement as a requirement for prompt-composed agent evaluation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 18 references that are in the map. First posted on arXiv 24 Jun 2026.
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- 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
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- 2023Skill-Mix: a Flexible and Expandable Family of Evaluations for AI modelscited by 64
- 2025Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Lengthcited by 18
- 2026MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systemscited by 11
- 2023Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptationcited by 10
- 2025QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AIcited by 6
- 2025Characterizing Pattern Matching and Its Limits on Compositional Task Structurescited by 4
- 2022Learning Label Modular Prompts for Text Classification in the Wildcited by 3
- 2023On Conditional and Compositional Language Model Differentiable Promptingcited by 2
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