Skip to content

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

Jul 2026 · 0 citations · 39 references
Computer Science

TL;DR

An empirical study of router-side injection in coding agents, examining four intervention levels of increasing subtlety and developing SIDEL, a framework for trace recording, replay, injection, and defense evaluation, which is evaluated four representative coding agents, and further evaluates whitelist-based execution control and LLM review.

Abstract

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.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem

A2M (Attraction-to-Manipulation), a two-stage black-box framework for hijacking MCP agents, is introduced, a two-stage black-box framework for hijacking MCP agents that motivate stronger tool vetting and runtime isolation in MCP ecosystems.

Lai-Zhen Li, Xuan Wang, Pei-Cheng Zhao et al. · 0 citations
#machine learning Preprint Aug 2026

AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes

Agentic Provenance (AgentProv), the first action-based identity audit for agentic LLM APIs, is introduced: AgentProv fingerprints a deployed model through its categorical tool-call distribution and decides identity via an MMD permutation test.

Xun Wang, Bihe Zhao, Michael Backes et al. · 1 citation
#artificial intelligence Preprint Aug 2026

AgentKernel: The Trust-Native Agentic Operating System

This work argues that agents need an operating-system substrate providing mandatory, non-bypassable services for identity, input mediation, memory governance, and execution control, and introduces AgentKernel, a trust-native agent operating system built around the premise that security must be a first-class design cons...

Zhen-Hua Zou, Sheng Guo, Qiu-Yang Zhan et al. · 0 citations
Book Open access Oct 2026

Introducing Safe LLM-Agent Execution via Typed Tool Mediation and World-Effect Evaluation

LLM-based agents are increasingly used as action-taking systems that operate over files, databases, networks, and external services. In this setting, safety failures can become unauthorized world effects, such as data deletion, protected-resource modification, or secret exfiltration, rather than merely unsafe text outp...

Jun-Hao Zhou, Tao Jia · 0 citations
#artificial intelligence Preprint Oct 2026

Securing Computer-Use Agents Against Branch Steering Attacks

Modern Computer Use Agents (CUAs) directly interact with graphical user interfaces and execute third-party web tools, exposing them to indirect prompt injection across every rendered page and tool response. While the Dual-LLM pattern is the primary system-level architecture offering formal security guarantees - using a...

Giulio Zingrillo, Hanna Foerster, Ilia Shumailov et al. · 0 citations
Preprint Aug 2026

WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents

This work proposes WebMCP-Phalanx, a dual-layer agent runtime architecture that provides a browser-native trust anchor that binds each tool to its registering principal through cryptographically protected capability credentials and propagates provenance labels throughout the tool lifecycle.

Lin-Fa Lee, Yi-Yu Chang, Kuo-Hui Yeh · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.