Use AI coding tools as assistants, not authorities: verify generated code and dependencies, and limit what an agent can read, run, change, or send. A suggestion-only assistant can produce flawed code; an agent with shell, filesystem, network, or repository access may also take actions using your permissions. Secure use requires checking both the output and the tool’s authority.
How AI coding tools can fail
There are two related but distinct risks: unreliable output and unwanted action. The first applies even when a tool only suggests text. The second grows when an agent can use tools or access external content. GitHub’s responsible-use guidance says Copilot Chat explanations may be inaccurate or incomplete, generated tests may miss scenarios, and generated code may be inaccurate or vulnerable. These limitations apply to review of the output, not just to whether it compiles.
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Plausible code can still be wrong
A model may produce code that looks convincing but does not meet the requirement, mishandles an edge case, or introduces a security weakness. Generated tests are also proposals: they may omit important cases or simply encode the same mistaken assumptions as the implementation. Passing tests—especially tests written by the same agent—does not independently establish that a change is secure.
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Dependencies can be invented or out of date
OWASP’s Secure Coding with AI guidance identifies two supply-chain hazards: a model can suggest a package name that does not correspond to a legitimate package, and it can recommend an old version with known vulnerabilities. Before adding a dependency, confirm that it exists on its official registry, inspect its origin and maintenance, and check the version against current project policy and security advisories. Pin and update versions through the project’s normal dependency-management process.
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Untrusted content can try to redirect an agent
Prompt injection can arrive indirectly in material the agent reads, not only in a prompt typed by a developer. An issue description, pull-request comment, README, changelog, error trace, webpage, or connected-tool output could contain instructions intended to change the agent’s behavior. NIST describes this kind of agent hijacking as indirect prompt injection: malicious instructions are placed in data an agent may ingest.
The practical consequence depends on the agent’s capabilities. A suggestion-only tool may return an unsafe proposal. A tool-enabled agent could act on an injected instruction using its existing access—for example, by running a command, editing files, or accessing network-connected tools. OWASP’s AI Agent Security guidance also identifies risks such as goal hijacking, tool abuse, data exposure, excessive autonomy, high-impact actions, and failures that cascade between agents.
Secure-use workflow
- Choose an approved tool and limit its context. Follow your organization’s rules for tools and data handling. Give the assistant only the files and project context needed for the task. Do not include secrets, personal data, or restricted material in a context that has not been approved. Product context and provider handling vary; check the current vendor documentation and settings that apply to your account.
- Constrain authority before enabling agent actions. Keep approval prompts for commands, network access, and writes outside the intended workspace enabled. Prefer a sandbox, development container, or disposable virtual machine for tasks that could have side effects. Do not give an agent production credentials; where credentials are necessary, use scoped, short-lived tokens. Allowlist and pin MCP servers and other connected tools rather than granting open-ended access.
- Handle material the agent reads as untrusted input. When asking an agent to process an issue, pull request, README, log, webpage, changelog, or tool output, assume it could include instructions aimed at the agent. Minimize the material provided, and watch for unexpected edits, commands, tool calls, or data access while it works. If agents pass content or summaries to one another, preserve the untrusted boundary and check that downstream actions remain within the original task.
- Verify behavior, tests, and dependencies independently. Compare the implementation with the requirements and project documentation. Inspect test assertions and coverage instead of relying on a claim that tests passed. Verify every new package and version, then run the project’s normal dependency and security checks. Keep existing tests unless a human reviews and approves a justified change.
- Review the complete change before accepting it. Examine the diff, commands run, dependencies added, permission changes, tests modified, and any output that left the environment. A named human should understand and approve the change before merge. Apply particular scrutiny to authentication, authorization, input validation, cryptography, secrets, and CI/CD.
Check whether a tool’s permissions fit the task
“AI coding assistant” does not describe one fixed level of access. OWASP notes that tool-enabled coding agents can execute commands, install packages, edit files, access networks, and push branches. GitHub documents agent mode as able to plan multi-step tasks and invoke tools such as terminal commands and file edits. Capabilities and settings can vary by product, platform, and configuration, so inspect the current documentation and the permissions actually enabled in your environment.
- Authority: Can the tool only suggest text, or can it run shell commands, write files, browse, call MCP tools, or push changes?
- Approvals: Are command, network, and write actions gated? Can the rules be centrally managed?
- Isolation: Can work happen in a sandbox, development container, or disposable VM without production credentials?
- Context and data handling: What repository files and terminal output may be shared? What exclusions and data-handling settings apply?
- Verification: Can a reviewer inspect changes as a diff and run the team’s usual tests, static analysis, software composition analysis, and secret scanning?
- Auditability: Can the team identify who approved a change and inspect the actions the tool took?
These are evaluation questions, not a ranking of products. Choose an access level proportionate to the task: an agent that can modify code or interact with other systems needs stronger boundaries and closer review than a tool that only offers suggestions.
Apply the same security gates as to human-written code
AI-generated changes should pass the project’s ordinary review and security process. OWASP’s DevSecOps guidance recommends applying security checks regardless of whether code was written by a person or assisted by AI. Use the checks appropriate to the project, including:
- Dependency auditing and software composition analysis for packages and versions.
- Static application security testing for code-level issues.
- Secret scanning for credentials or other sensitive values that may have entered a change.
- Tests reviewed for meaningful assertions and coverage of relevant cases.
- Human review of the diff and the agent’s actions, with stronger scrutiny for sensitive code paths.
These checks complement one another; a clean test run or a single scanner result is not proof that a change is safe. GitHub’s responsible-use guidance puts the review obligation plainly: “Always review and test code generated by GitHub Copilot Chat to ensure it meets your requirements and is free of errors or security concerns.” OWASP’s corresponding principle is: “AI-generated code must have a human owner.” That owner should review and approve the change and remain accountable for its security and maintainability.
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What a successful review should establish
Before merging, be able to explain what changed, why it meets the requirement, what new dependencies or permissions it introduces, and what checks were performed. Confirm that no unexpected command, file edit, network action, or data disclosure occurred. If the change touches a sensitive path or the agent’s actions are unclear, pause acceptance and investigate rather than treating a plausible explanation or passing tests as assurance.
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