mlsanityData to 14 Oct 2026

ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation

arXiv 2605.26542 ↗First posted 26 May 2026cs.CR cs.AIcited by 11

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Abstract (arXiv)

Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime. This creates a safety gap in tool composition: an agent can satisfy every per-tool permission check and still produce an unsafe end-to-end effect, such as reading a confidential document, summarizing it, and sending the summary to an external endpoint. We call this failure mode permission laundering. ChainCaps addresses it with a runtime rule: every value carries a sink-specific capability budget, and tool composition propagates budgets by intersection. A value can preserve or lose authority as it moves through a tool chain, but it cannot gain new authority through composition. We implement ChainCaps as a transparent MCP proxy that requires no changes to the agent or tool servers. On 82 tasks across five frontier models from three providers, ChainCaps reduces attack success rate from 25-68% to 0-4.8% while preserving 96-100% benign completion. In replay experiments, it also outperforms scalar-IFC and per-function-isolation baselines. Manifest quality is the dominant deployment bottleneck: expert manifests reach 100% attack blocking, while naive manifests fall to 27.3%. Our claims are limited to explicit-flow composition safety under trusted manifests and proxy-visible data movement, a practical gap in deployed tool-using agents today.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 26 May 2026.

Builds on

12 of 19 references.

References · 12

  1. 2025ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoningcited by 151
  2. 2025Securing AI Agents with Information-Flow Controlcited by 121
  3. 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agentscited by 87
  4. 2026Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Frameworkcited by 54
  5. 2025MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosiscited by 32
  6. 2025AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestrationcited by 25
  7. 2025MCP-Guard: A Multi-Stage Defense-in-Depth Framework for Securing Model Context Protocol in Agentic AIcited by 19
  8. 2026SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarkscited by 15
  9. 2026MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolutioncited by 12
  10. 2026Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflectioncited by 12
  11. 2026ChainFuzzer: Greybox Fuzzing for Workflow-Level Multi-Tool Vulnerabilities in LLM Agentscited by 9
  12. 2026SOK: A Taxonomy of Attack Vectors and Defense Strategies for Agentic Supply Chain Runtimecited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 11.

11 papers.

Similar, not cited

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  1. 2026Context-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps
  2. 2026From Tool Connection to Execution Control: Benchmarking Security Invariants in MCP-Style Agent Runtimes
  3. 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks
  4. 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
  5. 2026The Stochastic Deputy: Structural Tenant Isolation for Tool-Using LLM Agents
  6. 2026MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
  7. 2026When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling
  8. 2026Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement