From Approval to Execution: Reconstruction-Aware Repair Analysis for LLM-Agent Software
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Approval mechanisms have become a primary safeguard for consequential actions in LLM-agent software. Yet the action shown for approval is often not the object ultimately consumed: workflow reload, transcript projection, argument rebinding, and durable-state lookup may reconstruct it before execution. Existing fieldflow and check-coverage analyses can establish that expected fields were inspected, but not that the inspected object version reaches the sink or that no replacement intervenes between check and use. Consequently, a locally complete repair may still leave a residual authorization bypass after reconstruction. We address this problem through three designs. (1) We formulate reconstruction-stable authorization (ReSA) over representation transitions, sink dependencies, consumed versions, and grant scope. (2) We derive obligations that judge candidate repairs and expose residual sink suffixes. (3) We implement the analysis in APAS-Finder and freeze predictions before an independent sink oracle observes execution. Predictions agree with all 28 controlled outcomes. Four matched pairs require opposite judgments despite identical field-flow and check coverage; a CodeQL composite recovers all eight when supplied object flow, dominance, interference, and the same grant contract. Two analysts agree on all four model-validation dispositions and 19/20 sink dependencies. On released consumers, five repairs prevent 60/60 tested out-of-scope effects, while three mechanism contrasts expose the predicted residual effects. Repair sufficiency therefore depends on the reconstructed action consumed, not merely on an approval record or earlier checked representation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 6 of its 28 references that are in the map. First posted on arXiv 22 Sep 2026.
Builds on
6 of 28 references.
References · 6
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2026ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedbackcited by 64
- 2025AgentBound: Securing Execution Boundaries of AI Agentscited by 20
- 2017Identifying Patch Correctness in Test-Based Program Repaircited by 16
- 2026What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agentscited by 9
- 2026Loopjacking: Hijacking Human-in-the-Loop Approvalcited by 3
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- 2026Authorization Closure Graph: Minimal Repair for LLM Agents with Evolving User Instructions
- 2026REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows
- 2026Rebuild Dossier: Mechanically-Enforced Specs for Agentic App Rebuilds, and What Model-Tier Failures Reveal
- 2026StateWise: Diagnosing and Repairing Persistent Operational State Before Agent Actions
- 2026Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows
- 2026SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents
- 2026Structured Feedback Improves Repair in an LLM Agent Loop
- 2026Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows