mlsanityData to 7 Oct 2026

Where Is the Tradeoff in Using Third-Party API Routers for Agentic Software Development?

arXiv 2607.23624 ↗First posted 26 Jul 2026cs.SE cs.AI cs.CLcited by 0

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

Abstract (arXiv)

Third-party API routers have become a common layer that unifies access across increasingly diverse LLM providers. In coding-agent workflows, high-autonomy operation is widely adopted because it reduces interaction overhead. As a result, a third-party API router, which sits between the agent and the upstream provider, inevitably occupies the trusted path. It can inspect and modify every request and response, yet no mechanism verifies alignment between the provider's output and the repository-level actions ultimately executed by the agent. Consequently, client-side permission mechanisms may become ineffective in practice. Whether this control gap produces real, hard-to-detect effects on software development tasks remains empirically unmeasured. In this paper, we conduct an empirical study of router-side injection in coding agents, examining four intervention levels of increasing subtlety: Response Substitution (L1), Response Append (L2), LLM-Polished Injection (L3), and LLM-Polished with Distribution Alignment Injection (L4). Moreover, we develop SIDEL, a framework for trace recording, replay, injection, and defense evaluation, with a curated dataset of 400 samples. We evaluate four representative coding agents, and further evaluate whitelist-based execution control and LLM review. Router-side intervention substantially alters repository-level actions and remains difficult for existing client-side safeguards to detect. Without additional mitigations, all evaluated agents achieved a defense success rate of 0 percent across all injection levels. Client-side mitigations and reactive reviews improve resistance but do not fully restore end-to-end control, motivating provider-side output-integrity guarantees. Our code is available at https://github.com/Riyasushin/SIDEL.

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 32 of its 39 references that are in the map. First posted on arXiv 26 Jul 2026.

Builds on

32 of 39 references.

Closest ancestors · 13

Same direction, newest first

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  5. 2026Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain9 Apr
  6. 2026Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems3 Apr
  7. 2026Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks31 Mar
  8. 2026SoK: The Attack Surface of Agentic AI - Tools and Autonomy24 Mar
  9. 2026The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey11 Mar
  10. 2026ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback15 Jan
  11. 2025AgentBound: Securing Execution Boundaries of AI Agents24 Oct
  12. 2025Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment6 Sep
1 more
  1. 2025Prompt Injection Attack to Tool Selection in LLM Agents28 Apr

Other references · 19

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  3. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  4. 2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectoriescited by 75
  5. 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projectscited by 45
  6. 2024Human-In-the-Loop Software Development Agentscited by 42
  7. 2026Dive into Claude Code: The Design Space of Today's and Future AI Agent Systemscited by 33
  8. 2025Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wildcited by 11
  9. 2025Life-Cycle Routing Vulnerabilities of LLM Routercited by 10
  10. 2026DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollbackcited by 10
  11. 2026AgentCanary: A Security Evaluation Framework for Autonomous AI Agents in Real Executable Environmentscited by 10
  12. 2026Real Money, Fake Models: Deceptive Model Claims in Shadow APIscited by 8
7 more
  1. 2026Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skillscited by 8
  2. 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
  3. 2026Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimizationcited by 4
  4. 2026RerouteGuard: Understanding and Mitigating Adversarial Risks for LLM Routingcited by 3
  5. 2026Auditing MCP Servers for Over-Privileged Tool Capabilitiescited by 2
  6. 2026CacheProbe: Auditing Prompt Cache Isolation in Gateway APIscited by 1
  7. 2026The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEscited by 1

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