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Use AI-generated code as a proposal, not an authority: check its behavior, dependencies and security independently. If the tool can run commands, edit files or access a network, also limit what it can do and treat every issue, README, log and webpage it reads as untrusted input. A suggestion-only assistant can give you bad code; a tool-enabled agent may act on bad instructions with your permissions.
How are AI coding tools risky?
Two different failure modes call for two different checks. A model can produce code or explanations that sound convincing but are incomplete, incorrect, vulnerable or mismatched to your requirements. Separately, an agent that reads external or repository content may encounter instructions designed to redirect its actions.
The difference is authority. A suggestion-only assistant can hand you a flawed proposal. An agent with tools may also execute shell commands, install packages, edit files, access networks or push changes. GitHub’s documentation describes agent mode as able to plan multi-step tasks and invoke tools such as terminal commands and file edits; OWASP’s Secure Coding with AI Cheat Sheet describes the broader risks of coding agents with tool access.
| Configuration | What can go wrong | What to control |
|---|---|---|
| Suggestion-only assistant | Incorrect or insecure code, incomplete explanations, weak tests, or an unverified dependency recommendation. | Review the output, verify dependencies, and run independent checks before accepting changes. |
| Agent with repository or terminal tools | All of the above, plus unwanted file changes, command execution, network access or data exposure. | Restrict files, tools, credentials and network access; require approval for consequential actions; inspect the action history and diff. |
These are capability differences, not a ranking of products. Check what the particular tool and its current settings allow; capabilities and controls can vary by product, platform and configuration.
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What does an AI coding hallucination look like?
Plausible code that does not meet the requirement
Generated code may compile and still mishandle an edge case, violate a project convention or introduce a security flaw. Treat explanations as claims to verify against the code and project documentation—not as proof of what the code does.
Dependencies that are nonexistent or unsafe
A model may invent a package name. An attacker could register that name on a package registry and exploit developers who install it without checking. A model may also suggest an old version that has since acquired a known vulnerability. Confirm the package on its official registry, inspect its origin and maintenance, and check the version against current dependency-security information before adding it.
Tests that miss the important case
Generated tests can be incomplete or make the same mistaken assumptions as the generated implementation. A passing suite is useful evidence about the cases it covers, but it is not independent security assurance when the same agent wrote both the code and tests. Inspect the assertions and coverage, add tests for the actual requirements and edge cases, and preserve existing tests unless a human has reviewed a justified change.
Rank #2
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How can prompt injection manipulate a coding agent?
Prompt injection can arrive through content the agent reads, not just text you deliberately type into a prompt. An issue description, pull-request comment, README, error trace, changelog, fetched webpage or tool output can contain text that tries to redirect the agent. NIST’s January 2025 discussion of agent hijacking describes this kind of indirect prompt injection: malicious instructions are placed in data an agent may ingest, with the aim of causing unintended actions.
Reading an instruction does not make it trustworthy. Treat such content as data to analyze, not as permission to change the task, disclose information or take additional actions. The danger rises when the agent can carry out those instructions using its existing access. OWASP’s AI Agent Security Cheat Sheet identifies related risks including goal hijacking, tool abuse, data exposure and excessive autonomy.
- Limit the agent to the content and files needed for the task.
- Review unexpected tool calls, edits, permission changes and attempts to access or transmit data.
- Do not let an instruction embedded in external content expand the task or override your approval requirements.
- If one agent passes summaries or content to another, check that the original trust boundary remains clear and that the resulting actions are still in scope.
Can an AI coding tool expose secrets or project data?
Depending on the product and configuration, coding tools may send project context to a model provider. Context can include open files, repository structure and terminal output, which may reveal credentials, personal data, proprietary logic or internal architecture. A file being listed in .gitignore does not, by itself, establish that an assistant cannot read it.
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Before using a tool on sensitive work, check the provider’s current data-handling settings and context-exclusion controls. Follow your organization’s approved-tool and data-classification policies, keep secrets out of prompts and project files, and use an approved enterprise or self-hosted arrangement when policy requires it. Do not assume that products share the same context behavior or handling terms.
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- Choose an approved tool and a suitable data boundary. Follow your organization’s rules for tools and data. Give the assistant only the files needed for the task, and do not expose secrets or restricted material to an unapproved context. Confirm current product settings rather than inferring them from a repository convention.
- Constrain the agent before it acts. Keep approval prompts for commands, network access and writes outside the working area enabled. Use a sandbox, development container or disposable virtual machine where practical. Avoid production credentials; use scoped, short-lived tokens when credentials are necessary. Allowlist and pin MCP servers and other tools rather than granting broad, unchecked access.
- Mark content from outside the task as untrusted. When supplying an issue, pull request, README, webpage, log, changelog or tool output, remember it may contain instructions aimed at the agent. Minimize the context and inspect the agent’s actions for surprising edits, tool calls or data access.
- Verify behavior and dependencies independently. Compare the code with the requirements and project documentation. Confirm each package exists, check its origin and maintenance, and verify the proposed version. Run the project’s dependency and security analysis. Review generated tests for meaningful assertions and coverage instead of accepting a green result at face value.
- Review the complete change before merging. Examine the diff, files touched, commands run, dependencies added, tests altered, permission changes and any data sent outside the environment. A named human should understand and approve the change. Apply extra scrutiny to authentication, authorization, input validation, cryptography, secrets and CI/CD.
OWASP’s DevSecOps guidance recommends applying the same security gates to AI-assisted code as to other code, including static analysis, software composition analysis and secret scanning. These checks complement tests and human review; none substitutes for understanding the change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a team assess an AI coding tool?
Compare actual capabilities and controls in the intended configuration. Documentation and settings can change, so verify them for the product, platform and deployment your team will use.
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- Authority: Can it 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 organization manage those rules centrally?
- Isolation: Can work happen in a sandbox, development container or disposable environment without production credentials?
- Context and data handling: What repository and terminal content is shared, what exclusions are available, and which data classes are approved?
- Verification: Can the team inspect diffs and run its normal tests, static analysis, dependency analysis and secret scanning?
- Auditability: Can the team see tool actions and configuration, and identify the human who approved a change?
OWASP and GitHub documentation provide useful starting points for these questions, but the answer for your workflow depends on enabled tools, permissions and organizational policy.
Who owns an AI-assisted code change?
The developer or team accepting the change remains accountable for its security and maintainability. OWASP’s Secure Coding with AI Cheat Sheet puts it plainly: “AI-generated code must have a human owner.” A reviewer should be able to explain what changed, why it meets the requirement, and what checks were performed before approving it.
Quick Recap
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