mlsanityData to 7 Oct 2026

Execution-Grounded Security Testing for Coding Agents in Software Engineering Pipelines

arXiv 2607.22569 ↗First posted 1 Jun 2026cs.AI cs.SEcited by 0

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

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Abstract (arXiv)

Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, and the underlying system. For example, if a coding agent inserts a hook into a system startup or configuration script, that change can persist after the interaction, be triggered later, and abuse delegated user or system privileges to modify the system. This makes security testing a system problem: the key question is not only what the agent says, but what it actually does to the surrounding environment. We present an execution-grounded red-team testing framework for probing this execution-layer security boundary using observable sandbox evidence, including tool invocations, runtime traces, and file-system diffs. Our framework embeds target unsafe operations into routine software engineering workloads, including unit testing, regression testing, crash reproduction, and validation, and uses an execution oracle to guide refinement when an initial probe is rejected or fails. Across multiple agent frameworks and model backbones, our red-team workload reformulation substantially increases verified unsafe execution, reaching 73.61% on code carriers and 53.93% on text carriers. These results show that coding agents in system operations remain insecure under task disguise: once risky intent is hidden inside plausible engineering tasks, the agent can be induced to carry out unsafe actions on the surrounding system. More broadly, coding agents in system operations still demand stronger security testing and safeguards.

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 15 of its 27 references that are in the map. First posted on arXiv 1 Jun 2026.

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15 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents31 May
  2. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agents18 Jan
  3. 2023Testing Language Model Agents Safely in the Wild17 Nov

Other references · 12

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  4. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 551
  5. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  6. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 364
  7. 2023Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attackscited by 268
  8. 2023A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easilycited by 242
  9. 2024OpenCodeInterpreter: Integrating Code Generation with Execution and Refinementcited by 242
  10. 2023Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulationcited by 202
  11. 2023MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbotscited by 172
  12. 2024Leveraging the Context through Multi-Round Interactions for Jailbreaking Attackscited by 32

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  3. 2024RedCode: Risky Code Execution and Generation Benchmark for Code Agents
  4. 2026The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
  5. 2026When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems
  6. 2026MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
  7. 2026ABTest: Behavior-Driven Testing for AI Coding Agents
  8. 2026Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification