What fails when AI coding agents are judged only by functional correctness?

Why This Matters for Security Teams

Functional correctness answers only one question: did the code do what the prompt or test expected. For AI coding agents, that is not enough. An agent can generate working code that still weakens authentication, mishandles secrets, introduces unsafe deserialisation, or pulls in compromised dependencies. Security teams should treat the output as untrusted until it passes a separate security review aligned to OWASP Agentic AI Top 10 and related AI risk guidance.

The deeper issue is incentive design. When delivery teams are measured only by feature completion, agents learn the local objective: produce code that runs, not code that is resilient. That creates blind spots around prompt injection into developer tools, dependency confusion, insecure default settings, and hard-coded credentials in generated snippets. The right control is not to reject automation, but to define security correctness as a first-class release criterion alongside functional tests and code review. In practice, many security teams encounter the risk only after an apparently successful build has already reached the branch or package registry, rather than through intentional security gating.

How It Works in Practice

Security-correct evaluation for AI coding agents starts by separating “does it work?” from “is it safe to ship?” A good workflow uses functional tests for behaviour, then adds security checks for the generated artefact, the dependency graph, and the agent interaction trail. The NIST AI Risk Management Framework is useful here because it frames governance, mapping, measurement, and management as ongoing activities rather than one-time sign-off.

Operationally, teams should validate the agent’s output against several layers:

• Code quality and functional tests to confirm the intended feature still behaves as expected.
• Static and dependency analysis to catch vulnerable libraries, risky build steps, and transitive package exposure.
• Secrets scanning to block tokens, API keys, and certificates from entering source control or logs.
• Security review of authentication, authorisation, input validation, and data handling logic.
• Prompt and tool-use audit trails so reviewers can reconstruct why the agent generated a risky change.

This is where AI-specific threat frameworks matter. The MITRE ATLAS adversarial AI threat matrix helps teams think about manipulation paths that do not appear in ordinary software assurance, while the CSA MAESTRO agentic AI threat modeling framework is useful for mapping agent tool access, trust boundaries, and escalation paths. For developers building against agentic patterns, the OWASP Top 10 for Agentic Applications 2026 also provides a practical checklist for unsafe autonomy, excessive permissions, and poor output handling. These controls tend to break down when agents can commit directly to protected branches or publish dependencies without a human security checkpoint because the release pipeline treats code generation as equivalent to code trust.

Common Variations and Edge Cases

Tighter security review often increases release latency and review burden, requiring organisations to balance delivery speed against assurance. That tradeoff becomes sharper when teams rely on rapid prototyping, internal developer platforms, or high-volume code generation. Best practice is evolving, and there is no universal standard for the exact threshold that should trigger a security gate for agent-produced code.

Some environments need more than the usual controls. Regulated products may require additional sign-off for authentication changes, privacy-sensitive logic, or cryptographic handling. Legacy systems often create edge cases because generated code may be functionally correct but incompatible with older libraries or brittle deployment scripts. In AI-assisted refactoring, the most dangerous failures are often subtle: the agent preserves tests while changing trust boundaries, logging sensitive data, or adding a dependency that later becomes the weakest link.

Teams should also distinguish between developer productivity agents and production-facing agentic systems. A coding assistant in a sandbox is not the same as an autonomous agent with repository write access, CI/CD permissions, or secrets-management reach. Where the agent can act on behalf of a human or service account, identity and privilege controls become part of the security answer, not a separate concern. Current guidance suggests treating that expansion of authority as a governance event, not just a tooling upgrade.

Related resources from NHI Mgmt Group

• Why do AI coding agents increase supply-chain risk?
• What breaks when AI coding agents can read project setup metadata?
• How should teams reduce the blast radius of AI coding agents in production-adjacent systems?
• What do teams get wrong about AI coding agents generating access-related code?