[2607.12428] Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents

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arXiv:2607.12428 (cs)

[Submitted on 14 Jul 2026]

Title:Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents

Authors:A H M Nazmus Sakib, Dipayan Banik, Murtuza Jadliwala<br>View a PDF of the paper titled Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents, by A H M Nazmus Sakib and 2 other authors

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Abstract:The increasing adoption of autonomous coding agents accelerates software development but also introduces scoped security risks within high-impact file paths that can outpace traditional human review capacity. While prior research has primarily evaluated these systems in terms of functional correctness and productivity, this paper presents a large-scale empirical study using the AIDev dataset to systematically characterize security code smells in agent-generated pull requests (PRs). Through a combination of a validated LLM-as-a-judge framework and manual qualitative analysis, we identify and classify security misconfigurations across 16,112 file changes spanning 4,022 pull requests. Our results reveal that 38.9% of agent-generated PRs contain at least one security smell, with supply chain integrity issues accounting for 82.3% of all detected security smells. Furthermore, hard-coded credentials constitute 99.6% of all critical-severity security smells. Crucially, we find that human collaborators are responsible for introducing 67.6% of genuine leaked secrets within these agent-assisted workflows, while existing automated and human review processes fail to detect 81.1% of these credentials prior to integration. These findings highlight substantial security risks in agent-assisted software development workflows and suggest a potential reduction in developer vigilance. They also underscore the urgent need for context-aware security guardrails implemented directly at the point of human-AI collaboration.

Subjects:

Cryptography and Security (cs.CR)

Cite as:<br>arXiv:2607.12428 [cs.CR]

(or<br>arXiv:2607.12428v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.12428

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: A H M Nazmus Sakib [view email]<br>[v1]<br>Tue, 14 Jul 2026 06:59:41 UTC (4,354 KB)

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