mlsanityData to 7 Oct 2026

Safe to Check, Unsafe to Use: Relinking at the Compression Boundary of LLM Agents

arXiv 2606.21732 ↗First posted 19 Jun 2026cs.CR cs.AIcited by 0

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

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Abstract (arXiv)

Summarization-based prompt compression is increasingly used by LLM agents to shorten long, distributed contexts, but it shifts the security boundary: filters inspect the pre-compression prompt while the backend acts on a newly generated compressed context. We identify relinking, a compression-boundary vulnerability where the compressor behaves as a confused deputy, summarizing distributed, locally benign fragments into a complete malicious instruction. Unlike prompt injection, relinking need not place an explicitly malicious payload in the source context. We show that relinking arises from summarization itself: attention makes separated fragments jointly available, pre-training makes compatible fragments plausible to connect, and post-training favors compact backend-actionable summaries. We formalize the attacker-induced form as adversarial relinking and present Relink, an automated DSL-based tool that splits malicious payloads into benign fragments while keeping the complete payload absent before compression. Across four long-context agent benchmarks, Relink achieves 86.9% Relink Rate and Backend Action Rate versus 17.0% for clean-split controls. Existing defenses fail to reliably capture adversarial relinking; our KBRA defense reduces residual Backend Action Rate to 0.0%.

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 37 of its 54 references that are in the map. First posted on arXiv 19 Jun 2026.

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37 of 54 references.

Closest ancestors · 4

Same direction, newest first

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  3. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
  4. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 33

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  6. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
  7. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  8. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  9. 2022In-context Learning and Induction Headscited by 930
  10. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  11. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  12. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
21 more
  1. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 443
  2. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 343
  3. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 325
  4. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  5. 2023A Long Way to Go: Investigating Length Correlations in RLHFcited by 285
  6. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 279
  7. 2023Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attackscited by 268
  8. 2023Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Modelscited by 237
  9. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  10. 2025From LLM Reasoning to Autonomous AI Agents: A Comprehensive Reviewcited by 146
  11. 2025ACON: Optimizing Context Compression for Long-horizon LLM Agentscited by 126
  12. 2024DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakerscited by 91
  13. 2024Attention Tracker: Detecting Prompt Injection Attacks in LLMscited by 82
  14. 2025LLM Agents Making Agent Toolscited by 45
  15. 2024PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrievalcited by 30
  16. 2024FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMscited by 24
  17. 2025PromptLocate: Localizing Prompt Injection Attackscited by 23
  18. 2026Composer 2 Technical Reportcited by 20
  19. 2024From Single to Multi: How LLMs Hallucinate in Multi-Document Summarizationcited by 17
  20. 2022On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Methodcited by 12
  21. 2026GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)cited by 10

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