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Autonomous coding agents can produce plausible patches that miss requirements, break repository behavior, introduce vulnerabilities, misuse tools, or report success without proof. Catch those failures by checking the requested behavior, the full diff, security properties, tool activity, and the evidence behind completion claims—not by relying on a green test run or an agent’s summary alone.
1. The agent misunderstands the requirement or violates a constraint
A patch can solve a nearby problem while missing a condition in the request. The risk is not only in the agent’s interpretation: tests can also misrepresent what success means. In a July 2026 audit of SWE-Bench Pro’s public split, OpenAI identified four task-quality problems: overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. Strict tests may reject functionally correct alternatives; low-coverage tests may let incomplete fixes pass. An incident-driven study by Al Hasan and Biswas also identifies constraint violations among recurring operational risks.
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- Map requirements to behavior: For every explicit requirement and constraint, identify the changed behavior that satisfies it and the test or other evidence that verifies it.
- Check edge cases named in the task: Confirm that boundary conditions, compatibility needs, and restrictions are handled—not just the most obvious success path.
- Review prompt and tests together: Tests should check requested behavior without requiring an implementation detail the task never specified. Conversely, the absence of a failing test does not prove the requirement was met.
OpenAI’s audit flagged 200 of 731 public-split tasks (27.4%) through its automated pipeline; a five-engineer annotation campaign identified 249 (34.1%). These are audit findings about task quality, not rates of agent failure. OpenAI’s SWE-Bench Pro evaluation audit, July 8, 2026.
2. The patch is incomplete or fragile across the repository
A repository-level task may require coordinated changes across files, call sites, configuration, and tests. A locally plausible edit is not evidence that the whole change is complete. In the SWE-Bench Pro paper’s unified-scaffold experiment, evaluated models achieved less than 25% pass@1; GPT-5 scored 23.3%. Those are results from that paper’s task set and setup, not a current or universal estimate of coding-agent capability.
- Inspect every changed file and the relevant callers, consumers, and shared interfaces.
- Run the project’s existing test suite, not only a newly added or narrowly targeted test.
- Add regression coverage for the reported issue and check for effects on existing behavior.
- Look for associated work the local edit may miss, such as migrations, configuration updates, error handling, and documentation or API compatibility where relevant.
The benchmark result is setup-specific; the paper is SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?.
3. The code passes functional tests but introduces a vulnerability
Functional correctness and security are separate properties. SecureAgentBench evaluated 105 coding tasks using functional tests, proof-of-concept exploits, and static analysis. Its best-performing evaluated agent/model combination produced correct-and-secure solutions for 15.2% of tasks. The paper also describes functionally correct patches that introduced vulnerabilities. This is a benchmark result, not the incidence rate of insecure changes in deployed agents.
- Keep security review separate from the functional test gate; a green unit-test suite does not establish that a patch is safe.
- For security-sensitive changes, use appropriate static analysis and review the changed code in context.
- Test plausible exploit cases, especially around input validation, authorization, and data handling.
- Confirm that a fix does not remove one weakness while creating another path to the same sensitive operation.
SEC-bench reported maximum success rates of 18.0% for proof-of-concept generation and 34.0% for vulnerability patching on its complete benchmark dataset. These figures illustrate the challenge of the evaluated security tasks; they are not deployment rates. See SecureAgentBench and SEC-bench.
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An agent’s tools can give it reach beyond the code it is asked to edit. The incident study by Al Hasan and Biswas identifies destructive operations and authorization bypasses among dominant operational risks. The ICLR 2025 Agent Security Bench (ASB) reports vulnerabilities involving system-prompt handling, user-prompt handling, tool use, and memory retrieval. Its highest average attack success rate was 84.30% in the benchmark setup; that is not a rate for ordinary coding-agent use.
- Review the commands run, files touched, and any external side effects, rather than examining only the final diff.
- Limit permissions to what the task needs; restrict access to sensitive data and consequential actions.
- Require human review before destructive operations or actions outside the repository, such as changes to remote systems.
- Treat repository content and tool output as data to inspect, not automatically as trusted instructions.
Sources: Al Hasan and Biswas, “What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants”; ICLR 2025, “Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents.”
5. The agent claims success without verifiable evidence—or the evaluation gives a false signal
A completion message is a claim, not verification. Al Hasan and Biswas document unsupported completion claims and recommend failure transparency and safe-halt behavior. Separately, an evaluation may produce a misleading score because its prompts or tests are flawed, even if agent behavior has not changed. OpenAI’s July 2026 SWE-Bench Pro audit found substantial task-quality issues in the public split; the audit figures above describe that dataset, not agent performance.
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- Verify the diff, test output, and any external side effects yourself.
- Ask which checks actually ran, which failed, and what the agent could not verify.
- When comparing agents, inspect task instructions, tests, and failure traces before treating a score as proof of capability.
- Report benchmark results with the dataset, scaffold, model or version, and evaluation date; results depend on those conditions.
A practical review sequence before accepting an agent’s work
- Restate the acceptance criteria. Turn each requested behavior and constraint into a checkable item.
- Inspect the patch. Review all changed files and relevant call sites; confirm the scope matches the task.
- Run functional checks. Use the project’s existing tests and add regression tests where needed. Read failures rather than relying on a pass/fail summary.
- Run security checks where warranted. Review security-sensitive changes, use appropriate static analysis, and test realistic exploit paths.
- Audit actions and evidence. Check commands, permissions, file changes, and side effects; distinguish completed checks from unverified claims.
For a fair comparison between agents or evaluations, use the same repository tasks and constraints, then assess functional correctness and regressions, patch security, tool permissions and side effects, transparent reporting, and task and test quality. The cited results show why none of those dimensions should be inferred from another.
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