A software developer asks an AI coding agent to fix a customer account feature. Within minutes, the agent identifies the relevant files, updates the application, writes tests, and submits a proposed change. Everything appears to work. But the new code contains a flaw that allows one customer to access another customer’s information. The tests pass because they never checked whether the requesting user was authorized to view the records.
This example illustrates why agentic AI code security is becoming an important concern for software teams. Autonomous coding tools can handle increasingly complex development tasks, but their ability to generate functioning software does not guarantee secure results. Developers must consider not only code vulnerabilities but also the permissions, external information, and software packages available to the agents producing it.
AI Coding Agents Change Where Security Problems Begin
Traditional coding assistants primarily suggested snippets that developers could accept, modify, or reject. Agentic development tools can perform a much wider range of operations, including examining repositories, editing multiple files, running terminal commands, installing dependencies, and executing tests.
Some can also create branches, prepare pull requests, and respond to feedback without requiring a developer to perform every intermediate step. This changes how software is produced and how security reviews must operate.
A developer reviewing a short suggestion can inspect its immediate effect. Reviewing a larger agent-generated change requires understanding how multiple modifications interact. A seemingly routine feature update may alter authentication behavior, dependency versions, or deployment configuration.
The Open Worldwide Application Security Project (OWASP) addresses these concerns in its Secure Coding with AI Cheat Sheet. Its guidance identifies risks associated with autonomous tool execution, software dependencies, untrusted project instructions, and insufficient review of generated changes.
The central distinction is that a coding agent can introduce a security problem through its actions even when the code it writes appears reasonable.
When Repository Instructions Become an Attack Surface
AI coding agents often read project documentation to understand how an application works. They may examine README files, issue descriptions, pull request comments, configuration files, and documentation retrieved from external websites.
These sources help the agent complete legitimate tasks, but they are not necessarily trustworthy. An attacker who can modify a document or submit a repository comment may attempt to influence the agent by inserting instructions into that content.
This technique is known as indirect prompt injection. Instead of directly controlling the user’s request, an attacker places instructions in material the AI system is expected to read.
Consider an agent investigating a reported application error. The issue description contains legitimate details about the problem alongside a malicious instruction telling the agent to turn off a security check. If the agent treats that text as an authorized command, it may make changes unrelated to the developer’s actual request.
OWASP’s prompt injection prevention guidance explains why ordinary text filtering cannot fully defend against it. Attackers can disguise instructions, and language models may struggle to distinguish trusted commands from untrusted content.
Software teams therefore need controls outside the model itself. The surrounding development environment should enforce access permissions, approved tool operations, and restrictions on sensitive modifications.
The Risk of AI-Generated Software Dependencies
Modern applications depend on software libraries that perform tasks such as processing images, validating user input, connecting to databases, and managing authentication.
When a coding agent needs additional functionality, it may recommend installing an existing package. However, AI-generated recommendations can contain incorrect package names or references to outdated components.
One emerging security concern involves hallucinated dependencies. A model may suggest a package that does not exist in a public software registry. An attacker could register that name and publish malicious software, hoping a developer or automated agent installs it later.
This attack pattern is sometimes called slopsquatting. It differs from conventional typosquatting, where malicious packages use names resembling popular legitimate software.
Even when a suggested package exists, its presence in a registry does not establish that it is trustworthy. Dependencies can contain known vulnerabilities, malicious installation scripts, or security weaknesses introduced through compromised maintenance accounts.
Organizations should verify unfamiliar package names, evaluate maintainers and package histories, and check requested versions against available security advisories.
Automated dependency scanning can support this work, but it cannot establish that every dependency is safe. Security teams also need to consider how much access installed packages receive during development, testing, and deployment.
Why Functioning Code May Still Be Vulnerable
AI-generated code can satisfy a feature request while failing to enforce important security requirements. These failures may involve authentication, authorization, data validation, or application-specific business rules.
Imagine a business application that lets employees download invoices. A coding agent successfully creates the export feature and confirms that the resulting files contain the expected information.
However, the implementation checks only whether the user is logged in. It does not verify whether that employee is permitted to access the requested invoice.
The function works as intended for authorized users, but it also exposes information to people who should not receive it.
This is an authorization vulnerability, and it illustrates why functional correctness cannot be treated as proof of security. Tests that confirm a feature works under normal conditions may overlook what happens when someone intentionally misuses it.
The problem is not unique to AI-written software. Human developers can introduce the same weaknesses. The difference is that automated tools may produce substantial changes quickly, increasing the amount of code reviewers must examine.
Security requirements should therefore be established before implementation begins. For access-sensitive features, those requirements need to specify which users may perform each action and how the application must respond to unauthorized requests.
Agent Permissions Can Turn Coding Mistakes Into Security Incidents
The risks extend beyond generated application code. A coding agent may have access to local files, development credentials, cloud services, external tools, and repository administration functions.
If it operates with broad permissions, an incorrect instruction or compromised input may lead to unintended changes outside the requested task.
An agent working on a user interface should not automatically need access to production databases or deployment credentials. Similarly, an agent assigned to review a pull request may not require permission to modify repository security settings.
The principle of least privilege limits each system to the permissions necessary for its assigned work. Applying that principle to coding agents can reduce the consequences of mistakes and malicious instructions.
TechAmerica.ai has separately examined AI agent authorization and the importance of enforcing permissions independently of the agent’s own instructions.
For development environments, additional protections may include temporary credentials, restricted network access, isolated execution environments, and approval requirements for sensitive operations.
These controls do not make generated code secure by themselves. They limit the damage an agent can cause while it creates or tests code.
Passing Tests Is Not Enough When AI Writes the Tests
Software teams increasingly use AI agents to generate tests alongside application code. This can improve test coverage and reduce repetitive work, but it introduces a difficult question: who verifies that the tests assess the correct behavior?
An agent may produce code that contains a flaw and then write tests based on the same misunderstanding. The tests pass because they confirm the behavior the agent implemented, not necessarily the behavior the business requires.
Another concern involves modifications to existing tests. An automated tool attempting to resolve a failing build might weaken an assertion, replace a meaningful test with a simplified mock, or remove a test that exposes an unresolved problem.
These changes can create the appearance of improved software quality while reducing the ability to detect defects.
Independent validation is particularly important for authentication, authorization, payment processing, and other security-sensitive functions.
Reviewers should examine changes to tests as carefully as changes to application code. They should also include negative scenarios that verify how the system behaves when users provide invalid inputs, lack required permissions, or attempt prohibited operations. OWASP’ss guidance specifically warns against relying on test suites produced entirely by the same AI system responsible for the implementation.
How Security Checks Fit Into Automated Development
Automated security analysis can help teams identify certain problems before merging generated changes into a shared codebase.
Static application security testing examines source code for potentially dangerous patterns. Software composition analysis identifies dependencies with known vulnerabilities, while secret scanning looks for exposed credentials and other sensitive information.
These tools address different risks, and none provides a complete assessment on its own.
GitHub’s security guidance for AI coding agents describes ways to incorporate code, dependency, and secret scanning into agent-assisted workflows.
Organizations can also establish checks that identify unexpected changes to sensitive files. Examples include:
- Modifications to authentication and authorization logic.
- New dependencies or changes to package installation scripts.
- Updates to deployment workflows, infrastructure configuration, or build permissions.
- Changes that remove tests or weaken security assertions.
- New access to environment variables, credentials, or external network services.
Such checks help reviewers concentrate on changes with potentially significant consequences. However, an automated scanner may not recognize a business logic vulnerability that depends on how a particular application is intended to operate.
That limitation is why security testing should also include behavior-based evaluation of the running application.
Automated Security Fixes Need Their Own Verification
AI agents can help developers investigate vulnerabilities and propose patches. A model may identify a problematic code path, suggest a correction, and update related tests.
These capabilities can be useful when organizations face substantial maintenance work. Nevertheless, a proposed fix can introduce a different vulnerability, alter intended functionality, or create compatibility problems.
For example, an agent might address an input-validation issue by rejecting a broad range of requests. The change could block a known malicious input while also preventing legitimate customers from completing transactions.
Another patch might repair one vulnerable endpoint without addressing similar behavior elsewhere in the application.
A meaningful security review must therefore examine whether the patch resolves the original problem and whether the modified application still behaves correctly.
For higher-risk changes, independent testing may include security-focused integration tests, controlled adversarial testing, and manual examination of the affected business logic.
The objective is not simply to generate a patch quickly. It is to demonstrate that the correction addresses the vulnerability without creating unacceptable new risks.
NIST’s Software Security Framework Still Applies
AI coding agents introduce new development patterns, but established secure software engineering principles remain relevant.
The National Institute of Standards and Technology’s Secure Software Development Framework recommends integrating security practices throughout the software development life cycle.
Its guidance covers preparing organizations for secure development, protecting software components, producing secure software, and responding to vulnerabilities.
NIST has also been developing updated DevSecOps guidance that addresses AI-supported development and the risks associated with autonomous systems.
For organizations adopting coding agents, these principles support documented security requirements, controlled development environments, traceable changes, and pre-release verification.
They also reinforce a distinction between using automation to perform tasks and delegating accountability for the resulting software.
A coding agent can prepare a change, but the organization deploying that change remains responsible for deciding whether it meets the required standards.
What Human Review Should Focus On
Human oversight becomes particularly valuable when a change affects security boundaries, customer information, financial operations, or production infrastructure.
Reviewers need enough information to understand what the agent changed and why. That includes the original task, the relevant code differences, newly introduced dependencies, and the results of security testing.
Reviewing only an AI-generated summary is insufficient because it may omit unexpected modifications or misinterpret their significance.
Teams should also establish clear ownership for generated code. Someone must be accountable for investigating failures, approving sensitive changes, and maintaining the software after deployment.
The appropriate level of review depends on risk. Updating explanatory text in an application may require less scrutiny than modifying account permissions or payment authorization logic.
Organizations can use this distinction to prevent routine work from overwhelming security teams while preserving stronger controls for consequential changes.
Secure AI Development Requires More Than Faster Detection
Agentic AI code security is not only a question of whether automated scanners can find vulnerabilities faster than before. It involves protecting the entire process through which autonomous tools interpret instructions, obtain dependencies, modify code, execute commands, and prepare software for release.
The most difficult problems often arise at the boundaries between these activities. An external document may influence an agent’s actions, a dependency may execute untrusted code, or a generated feature may pass ordinary tests while violating a business rule.
These risks cannot be resolved solely by asking an AI system to write more secure code. Organizations need enforceable permissions, trustworthy development environments, independent validation, and clear human accountability.
As coding agents become more capable, software teams will increasingly need to evaluate the development process as carefully as the finished application. Producing code quickly is valuable, but its business value depends on whether it can be trusted when it reaches real users.
