Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoning
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning largely focuses on attacker-controlled injection and disguise, but developers also shape risk through everyday invocation choices: what task to delegate, how to phrase the request, and which skills or rules to supply. We term these user-side choices Prompt-Level Configurations (PLCs) and introduce CIPR (Coding In Poisoned Repos), the first benchmark that systematically varies PLCs in poisoned real-world repositories. CIPR comprises 1,920 instances across 20 repositories, four task types, three social-media-grounded prompt styles, and three skill/rule conditions, and measures attack success rate (ASR) and agent alert rate (AR) using automated runtime and trace-based oracles. Our evaluation reveals two key insights: (1) Vulnerability is highly context-dependent, with task type creating up to a 4.5-fold difference in ASR, with test-execution task forming a silent attack surface (high ASR, low AR). (2) Prompt expression shifts risk indirectly: underspecified prompts reduce ASR by truncating execution depth; noisy prompts exhibit a directional trend toward suppressing alerts by making malicious content less conspicuous. These findings highlight that coding agent vulnerability is not a static property, but a dynamic outcome shaped by everyday user configurations.
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 23 of its 37 references that are in the map. First posted on arXiv 31 Aug 2026.
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23 of 37 references.
Closest ancestors · 10
Same direction, newest first
- 2026Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems3 Apr
- 2026The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities26 Mar
- 2026You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents12 Mar
- 2026Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis of Vulnerabilities in Skills, Tools, and Protocol Ecosystems24 Jan
- 2025Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks2 Dec
- 2025QueryIPI: Query-agnostic Indirect Prompt Injection on Coding Agents27 Oct
- 2025Takedown: How It's Done in Modern Coding Agent Exploits29 Sep
- 2025Defeating Prompt Injections by Design24 Mar
- 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents17 Mar
- 2024EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage17 Sep
Other references · 13
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
- 2021Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Modelscited by 227
- 2024ProSA: Assessing and Understanding the Prompt Sensitivity of LLMscited by 145
- 2025Securing AI Agents with Information-Flow Controlcited by 108
- 2022How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Modelscited by 43
- 2025When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptionscited by 8
- 2018Consequences and Factors of Stylistic Differences in Human-Robot Dialoguecited by 4
- 2025Code Roulette: How Prompt Variability Affects LLM Code Generationcited by 4
- 2025More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generationcited by 4
- 2026When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generationcited by 3
- 2024Six Million (Suspected) Fake Stars in GitHub: A Growing Spiral of Popularity Contests, Spams, and Malwarecited by 2
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