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AI coding agents can install dependencies in some workflows, but whether they do—and what they can reach—depends on the product, task, permissions, and execution environment. The safest approach is to verify a proposed package’s exact name, source, and version before it runs; sandboxing and vulnerability scans are useful, separate safeguards, not proof that a dependency is trustworthy.
Can AI coding agents install malicious dependencies?
They can be directed to install an unverified dependency, but that does not mean every agent does so by default. Anthropic documents installing Claude Code through npm, while a security study describes tested agents reading setup documentation and executing package-install commands. Behavior varied by harness-model configuration, so the study does not establish an industry-wide failure rate or predict what every agent will do. Anthropic’s Claude Code installation documentation also warns: “Do NOT use sudo npm install -g as this can lead to permission issues and security risks.” The study concludes that install-time security depends on the harness-model combination, not the model alone.
Myth 1: Agents never install dependencies without me
That claim is too broad. An agent may follow repository setup instructions and run a package manager when the task and environment permit it. Whether it needs approval, can access the package manager, or can reach a registry depends on the specific tool configuration. Check the product’s current documentation and the settings for the environment you are using rather than assuming either that every install requires confirmation or that installs always happen automatically.
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Myth 2: If the README says to install it, the package must be legitimate
Repository setup instructions are input to verify, not proof of package identity. The study documents attacks embedded in ordinary setup documentation, including instructions to use untrusted registries, known-vulnerable versions, or plausible but incorrect package names. A typo or lookalike name can point to a different package than the one a developer intended.
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Check the proposed dependency before it runs
- Compare the exact package name with the project’s stated dependency and a trusted upstream source.
- Confirm the registry or other source matches what the project intends; do not accept a different registry just because a README requests it.
- Check the requested version and whether it is the expected release.
- Review the install command and any lifecycle scripts that may execute during installation.
The study found deterministic pre-install checks of package name, source, and version effective as a mitigation in its evaluation. That result supports a verification step; it is not a guarantee that a checked package is harmless.
Myth 3: A sandbox makes package installation harmless
A sandbox can limit access to the host or restrict network connections, but it does not verify a package’s publisher, identity, or code. Host isolation, outbound network access, dependency trust, and integration channels are distinct controls. For example, Anthropic documents network settings that can range from no network access to package-manager access or broader domain access. GitHub describes its cloud-agent environment as ephemeral and firewalled. Those boundaries affect what an agent can reach; they do not establish that a dependency is authentic or safe. Anthropic’s security documentation and GitHub’s cloud coding-agent documentation describe their respective environments.
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Myth 4: A clean vulnerability scan means the dependency is safe
A clean scan means only that the dependency passed the checks the scanner actually performs. GitHub says its relevant workflow checks newly introduced dependencies against the GitHub Advisory Database for malware advisories and high or critical vulnerabilities. That is a defined scope, not a universal test for every malicious, compromised, unsuitable, or newly reported package. Use advisory scanning as one layer alongside package identity checks and review of installation behavior. GitHub documents the scope of dependency review.
Myth 5: All coding agents install packages the same way
Products and deployment modes differ. Compare the configuration in use, not a blanket claim about “AI agents.”
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| Product or mode | What the cited documentation establishes | What to verify for your setup |
|---|---|---|
| Claude Code | Anthropic documents npm installation and other installation methods; its documentation describes configurable network access. | Current installation method, permissions, and network settings. |
| OpenAI Codex cloud launch configuration | OpenAI’s launch announcement described a cloud setup with pre-installed dependencies and internet access disabled. This is a launch-era configuration, not evidence of every current Codex configuration. | Current environment setup, available package managers, and internet settings. |
| GitHub coding agent | GitHub documents cloud-agent and CLI modes, including an ephemeral, firewalled cloud environment. | Which mode is active and the network and approval controls that apply. |
For these distinctions, see Anthropic’s installation documentation, OpenAI’s Codex launch announcement, and GitHub’s coding-agent documentation. Product documentation can change; confirm the settings for your version and deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I stop a coding agent from installing an unverified package?
- Inspect the proposed change. Before installation, identify the exact package name, source or registry, and version. Compare them with the project’s intended dependency.
- Review the install command. Look for unexpected registries, version substitutions, and lifecycle scripts that may run code during installation.
- Limit network access. Configure egress for the task’s needs; if package installation is unnecessary, disable access to package registries where the environment allows it.
- Use approval controls. Where available, require review before the agent runs installation commands or changes dependency files.
- Run dependency checks. Use advisory scanning to identify covered malware advisories and vulnerabilities, while recognizing that scan results cover only the scanner’s stated scope.
These controls reduce different risks; none guarantees that a dependency is safe.
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