AI coding assistants can help developers write functions, generate tests, explain unfamiliar code, and build features faster. Tools that support agentic workflows can go further by editing multiple files, running terminal commands, installing dependencies, and interacting with external services.
But faster development does not automatically mean safer software.
AI-generated code can contain security vulnerabilities, expose sensitive information, or introduce dependencies that have not been properly reviewed. When coding agents have access to repositories, terminals, credentials, and external tools, a mistake or malicious instruction can also affect more than the code they generate.
The OWASP Secure Coding with AI Cheat Sheet highlights risks associated with AI-assisted and agentic development, including insecure code generation, prompt injection, excessive permissions, and exposure of sensitive information.
Understanding these risks helps engineering teams adopt AI coding tools without treating their output as automatically secure.
What Are AI Coding Security Risks?
AI coding security risks are vulnerabilities and security exposures that can arise when developers use AI systems to generate, modify, review, or execute software code.
Some risks come from the code itself. An AI assistant might generate an insecure authentication flow or an SQL query that allows injection. Others emerge from how the AI tool operates, such as sending confidential code to an external service or following malicious instructions hidden in a repository.
These risks can be grouped into three broad categories:
- Code-level risks: Vulnerabilities introduced into application code, configuration files, or infrastructure definitions.
- Data and supply chain risks: Exposure of secrets, confidential information, or reliance on untrusted packages and tools.
- Agent-level risks: Prompt injection, excessive permissions, unsafe tool execution, and actions performed without adequate oversight.
The distinction matters because reviewing generated code alone cannot address every threat. Teams also need controls around the AI tools, data, dependencies, and execution environments involved in development.
8 AI Coding Security Risks Developers Should Know
1. Insecure AI-Generated Code
One of the most direct risks is that AI can generate code that works as intended in a simple example but contains security weaknesses.
AI models learn patterns from existing code and other training material. They can reproduce common implementation patterns without reliably determining whether those patterns are appropriate for a particular application's security requirements.
Examples include:
- SQL queries built through unsafe string concatenation.
- Missing authentication or authorisation checks.
- Improper input validation and output encoding.
- Unsafe handling of file uploads.
- Weak or outdated cryptographic methods.
- Insecure defaults in application configuration.
Consider an API endpoint that returns customer information. An AI assistant might implement the database query correctly but fail to verify whether the requesting user is authorised to access the selected customer's record.
The endpoint may pass basic functional tests while exposing information that should remain private.
How to reduce the risk
- Review AI-generated code before merging it.
- Run static application security testing (SAST) and relevant automated tests.
- Verify authentication, authorisation, and input validation explicitly.
- Test security-sensitive behaviour, not just whether the feature works.
- Apply the same security standards to AI-generated and human-written code.
AI can help developers implement a solution, but security requirements still need to be defined and verified.
2. Prompt Injection Against AI Coding Agents
Prompt injection occurs when malicious instructions are embedded in content that an AI system processes, potentially influencing it to act outside its intended task.
In coding workflows, an agent may read repository files, issue descriptions, pull request comments, documentation, package instructions, or external web pages. Any of these sources could contain untrusted instructions.
For example, a coding agent might be asked to fix a bug in an open-source repository. A malicious instruction hidden in a project file could attempt to persuade the agent to expose environment variables, modify a security configuration, or execute an unrelated command.
The danger increases when the agent can act on its environment rather than simply return text.
Prompt injection is not limited to malicious user prompts. It can also come from indirect sources the developer did not realise would influence the agent.
How to reduce the risk
- Treat repository content and external instructions as untrusted input.
- Restrict the agent's access to files, tools, and network resources.
- Require additional review for changes to authentication, deployment, and security configuration.
- Keep confirmation requirements enabled for sensitive actions.
- Never rely on a prompt alone to prevent access to restricted resources.
The objective is to limit what an agent can do if it follows a malicious instruction.
3. Secret and Sensitive Data Leakage
AI coding tools may process source code, configuration files, logs, prompts, and other information that contains sensitive data.
Depending on the tool and its configuration, information submitted to an AI service may be transmitted to an external provider. Risk also arises when an agent reads local secrets or passes them to a tool, log, generated file, or external destination.
Examples of sensitive information include:
- API keys and access tokens.
- Database credentials.
- Private keys and certificates.
- Customer records and personal information.
- Proprietary source code.
- Internal infrastructure details.
A developer might paste a production error log into an AI assistant without noticing that it contains an access token. An agent might also read a local environment file when troubleshooting a configuration issue and accidentally include its contents in a response.
How to reduce the risk
- Follow an explicit policy for which information can be shared with AI tools.
- Never place real credentials in prompts or source code unnecessarily.
- Use secret-scanning tools before code is committed.
- Keep secrets in approved secret-management systems.
- Limit access to credentials and use short-lived, narrowly scoped tokens where possible.
- Review the data handling, retention, and access policies of AI providers before using them with sensitive material.
Organisations should evaluate the actual configuration of each tool rather than assume that every AI service handles submitted data in the same way.
4. Vulnerable or Hallucinated Dependencies
AI assistants can recommend packages, libraries, and frameworks to solve a coding problem. However, a suggested dependency may be outdated, vulnerable, unsuitable, or nonexistent.
A particularly concerning scenario is package hallucination. An AI model may suggest a package name that appears plausible but does not correspond to a legitimate project. If an attacker registers that name and a developer installs it, the application could inherit malicious code.
Even a real package can introduce risk if it has known vulnerabilities, excessive permissions, an untrusted maintainer, or a compromised release.
This is a software supply chain problem: the application can become vulnerable through components introduced during development, even if the new code looks reasonable.
How to reduce the risk
- Verify that every new dependency exists and comes from the intended maintainer.
- Check package reputation, maintenance activity, and release history.
- Use software composition analysis (SCA) to identify known dependency vulnerabilities.
- Pin and review dependency versions where appropriate.
- Prefer trusted package registries and follow organisational dependency policies.
- Avoid installing packages automatically just because an AI assistant recommends them.
Dependencies should be evaluated as part of the application's attack surface, not treated as harmless implementation details.
5. Unsafe Tool Execution and Excessive Permissions
AI coding agents may use terminals, modify files, execute scripts, install packages, or connect to external services.
These capabilities can save time, but they also create opportunities for destructive or unauthorised actions when an agent has more access than its task requires.
For example, an agent asked to run a local test might execute a command that deletes files, modifies a shared environment, or attempts to access cloud credentials. The impact depends on the permissions available to the agent and the controls enforced by the environment.
Running an agent with the same unrestricted privileges as a developer can turn a limited mistake into a broader security incident.
How to reduce the risk
- Run agents in isolated development containers or sandboxed workspaces where practical.
- Use least-privilege permissions and restrict access to sensitive directories.
- Disable unnecessary network access.
- Separate development credentials from production credentials.
- Require approval for deployments, destructive commands, and other high-impact operations.
- Apply resource limits and logging to agent execution environments.
A coding agent should have the minimum permissions needed to complete its assigned task, not unrestricted access by default.
6. Insecure Configuration and Infrastructure as Code
AI tools can generate configuration files for cloud services, containers, CI/CD pipelines, identity permissions, and infrastructure deployments.
These files can introduce serious weaknesses even when the application code itself is secure.
Examples include:
- Publicly accessible storage that should be private.
- Overly broad cloud IAM permissions.
- Containers running with unnecessary privileges.
- Debug settings enabled in production.
- Secrets embedded in deployment files.
- CI/CD workflows that execute untrusted code with privileged credentials.
Configuration errors can be particularly difficult to spot because the application may function normally during development.
How to reduce the risk
- Scan infrastructure as code and configuration files for security issues.
- Review cloud permissions and network exposure before deployment.
- Validate CI/CD workflows and protect sensitive pipeline variables.
- Test production-like configurations in a controlled environment.
- Require specialised review for changes to access policies, deployment workflows, and security controls.
AI-generated infrastructure should go through the same review and approval process as manually written configuration.
7. Over-Reliance on AI Code Reviews
AI can assist with code review by identifying suspicious patterns, explaining changes, and suggesting improvements. However, an AI reviewer can also miss vulnerabilities or produce findings that are incorrect.
A dangerous workflow occurs when developers treat an AI review as definitive proof that code is secure.
For example, an assistant may confirm that an endpoint validates input but fail to notice that it does not enforce resource ownership. It might also overlook a security issue that depends on how several components interact.
Using AI to review AI-generated code can be useful, but two AI-assisted steps do not automatically provide independent verification.
How to reduce the risk
- Use AI review as a supporting layer rather than the only security control.
- Run independent SAST, SCA, and secret-scanning tools.
- Require human review for sensitive code paths.
- Test authorisation, business logic, and security boundaries explicitly.
- Investigate disagreements between AI review results and deterministic security checks.
The aim is to combine AI-assisted analysis with tests, established security tooling, and human judgement.
8. Unreviewed AI-Generated Changes and Hidden Regressions
AI can produce changes across multiple files quickly. That speed can increase the amount of code a developer needs to inspect and make it easier for unintended changes to slip through.
An agent might solve the requested problem while also modifying unrelated functions, changing a permission check, updating a dependency, or altering a test in a way that hides a failure.
The risk is not simply that the AI writes bad code. It is that developers may approve a larger or less-understood change than they would normally accept.
How to reduce the risk
- Keep tasks and code changes narrowly scoped.
- Inspect the full diff, including configuration and test changes.
- Check for unrelated edits and unexpected dependency changes.
- Protect critical files with required code-owner approval.
- Run regression tests before merging.
- Make sure developers understand the changes they approve.
AI-assisted development should make changes easier to produce, not harder to understand.
A Practical Checklist for Secure AI-Assisted Coding
Engineering teams can reduce risk by adding security checks throughout the development lifecycle.
Before Using AI Coding Tools
- Establish an approved list of AI coding tools and their permitted use cases.
- Define which data can and cannot be shared with external AI services.
- Review provider data-handling and retention settings.
- Decide which repositories and environments agents are allowed to access.
- Establish permission requirements for terminal execution, network access, and external integrations.
While Generating and Modifying Code
- Provide clear requirements, including relevant security constraints.
- Treat untrusted repository content and external instructions cautiously.
- Verify new dependencies before installing them.
- Avoid exposing secrets or unnecessary confidential data to the model.
- Restrict the scope of changes and review unexpected modifications.
- Require approval for sensitive or destructive operations.
Before Merging or Deploying
- Run automated tests and inspect their coverage.
- Run SAST and dependency vulnerability scanning.
- Scan for exposed secrets.
- Review infrastructure, authentication, and authorisation changes.
- Inspect the full code diff and investigate unexpected behaviour.
- Require human approval for high-risk changes.
- Verify that deployment permissions and production credentials remain protected.
This checklist should be adapted to the project's risk level. A prototype with synthetic data may need fewer controls than a production application handling financial or personal information, but neither should treat AI-generated code as inherently trustworthy.
How to Build a Secure AI Coding Workflow
Security is most effective when it is built into the development process instead of being added only after a problem is discovered.
1. Define an AI Usage Policy
Specify which tools developers can use, what information they may submit, and what types of tasks require additional approval.
The policy should be practical enough for developers to follow during everyday work. It should also clarify responsibility: developers remain accountable for the code they approve and deploy.
2. Apply Security Checks Automatically
Integrate automated security checks into the editor, local development workflow, and CI/CD pipeline where appropriate.
SAST can identify certain insecure code patterns, SCA can detect known dependency vulnerabilities, and secret scanning can flag potential credentials. Infrastructure scanning can identify configuration weaknesses before deployment.
These tools have limitations, so their findings should be evaluated in context. However, automated checks help ensure that security validation is not skipped simply because code was generated quickly.
3. Control Agent Permissions
Use the principle of least privilege to limit an agent's access to the resources required for its task.
Separate development environments from production, restrict access to credentials, and require confirmation for sensitive operations. Review third-party tool integrations and MCP servers before granting them access to files, networks, or credentials.
Where possible, enforce restrictions through the environment rather than relying only on written instructions.
4. Keep Human Review in the Workflow
Human review remains important for authentication, authorisation, cryptography, payment processing, sensitive data handling, and other high-impact areas.
Reviewers should evaluate whether the change meets the requirements, introduces unintended behaviour, and follows the project's security standards.
The goal is not to manually inspect every generated line with equal intensity. It is to focus review effort where the consequences of an error are greatest.
5. Monitor and Improve
Track recurring vulnerabilities, security scan findings, dependency problems, and the types of mistakes introduced by AI-assisted changes.
Use these findings to improve repository instructions, tests, tool permissions, and review practices. If a class of vulnerability keeps appearing, strengthen the corresponding guardrail or automated check instead of relying only on developers to remember another instruction.
Frequently Asked Questions
What are the biggest security risks of AI coding?
Major risks include insecure generated code, prompt injection, secret leakage, vulnerable dependencies, excessive agent permissions, insecure infrastructure configuration, and insufficient review of AI-generated changes.
Can AI-generated code contain security vulnerabilities?
Yes. AI models can generate code with weaknesses such as SQL injection, broken access control, insecure input handling, and weak cryptographic choices. Generated code should be reviewed and tested using the same security standards applied to other code.
What is prompt injection in AI coding?
Prompt injection occurs when malicious instructions embedded in content processed by an AI system influence its behaviour. In coding workflows, these instructions might appear in repository files, issue descriptions, documentation, or other untrusted content an agent reads.
How can developers prevent secrets from leaking into AI tools?
Developers should follow approved data-handling policies, avoid placing credentials in prompts, use secret-management systems, scan code for exposed secrets, and limit agents' access to sensitive files. They should also understand the data retention and processing settings of the AI services they use.
Should AI-generated code always be reviewed by a human?
AI-generated code should be appropriately reviewed before being merged or deployed. The depth of review depends on the risk of the change, but security-sensitive functionality and high-impact operations require particular care. Automated tests and scanners complement human review rather than replacing it.
Are AI coding agents riskier than AI code completion tools?
They can introduce additional risks because agents may edit multiple files, run commands, install packages, access external tools, and act with the developer's permissions. The level of risk depends on the agent's capabilities, the environment, and the restrictions in place.
Which tools help secure AI-generated code?
Useful controls include static application security testing (SAST), software composition analysis (SCA), secret scanning, infrastructure-as-code scanning, automated tests, and code review tools. Sandboxed environments, scoped credentials, and permission controls are also important for autonomous coding agents.
Does using a more advanced AI model guarantee secure code?
No. A more capable model may perform better on some tasks, but it does not guarantee secure implementation. Security still depends on requirements, context, dependencies, execution controls, testing, and review.
Use AI to Accelerate Development Without Skipping Security
AI coding tools can help teams build software faster, but speed is only valuable when the resulting software remains reliable and secure.
The strongest approach combines AI-assisted development with established software engineering practices: clear requirements, least-privilege access, dependency verification, automated security checks, and meaningful human review.
For businesses adopting AI-assisted development, these safeguards should be part of the workflow from the beginning, not an afterthought once the code is ready for release.
Tech for Hire helps businesses explore AI-augmented engineering workflows that combine AI capabilities with practical software development expertise. Learn more about AI-Augmented Engineers and how AI can support development while keeping code quality, security, and maintainability in focus.