You can use an AI assistant to help investigate vulnerabilities, but no prompt or privacy setting alone makes sensitive code safe to share. First classify the material and confirm the exact tool, account, and configuration are approved for it. Then limit what the assistant can receive and do, treat repository content as untrusted, and verify every finding independently.
Can you paste proprietary code into an AI assistant?
Only if your organization has approved that specific assistant, account tier, and configuration for the code’s classification. “Proprietary” can include source code, architecture details, customer data, internal reports, or other confidential business information; the material may also contain credentials or personal data. A consumer account’s general privacy wording is not, by itself, organizational approval.
Before sharing code, identify what the request actually contains and what rules apply to it. If it includes regulated, classified, customer, or otherwise highly sensitive material, do not send it to an unapproved service. For especially sensitive work, a self-hosted or air-gapped coding tool may be an option, subject to organizational approval and operational review.
Does a coding assistant send the whole repository?
Not necessarily, but do not assume it sends only the text visible in the editor. Depending on the tool and configuration, context may include open files, indexed repository content, attached files, terminal output, retrieved material, memory, or information available through agents and plugins. Check the official documentation and settings for the exact product and account you use; behavior and terms can vary by provider, plan, and configuration.
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Review what is transmitted, where it goes, how long it is retained, whether it may be used for training, and which access, deletion, and residency controls apply. In a team deployment, include logging and administrative access in that review. OWASP’s Secure Coding with AI Cheat Sheet warns that assistants can use broader context than the current file.
Do not rely on .gitignore to keep files away from an assistant. It governs Git behavior; it does not prevent a program that can read the project directory from accessing a file on disk. Use the assistant’s own documented context-exclusion mechanism, and verify that it applies to the relevant features, such as repository indexing or terminal context.
How to use AI for vulnerability research with less exposure
- Classify the material and confirm approval. Check code, logs, reports, and vulnerability details for credentials, personal or customer data, confidential business information, proprietary code, and regulated or classified content. Confirm that your organization permits the exact tool and configuration to process that category.
- Map the assistant’s context and data handling. Check whether the tool can access open files, the repository, terminal output, attachments, retrieval, memory, agents, or plugins. Review transmission destination, retention and deletion, training-use terms, residency, and access controls in the current product documentation.
- Minimize and sanitize the input. Share the smallest excerpt that can answer the question. Remove credentials, tokens, private keys, customer identifiers, and unrelated proprietary details. If values or relationships matter to the analysis, replace sensitive values with consistent placeholders while preserving the relevant structure. OWASP’s LLM02:2025 Sensitive Information Disclosure identifies sanitization and input validation as mitigations for disclosure risk.
- Exclude secrets and sensitive paths. Configure the assistant’s supported exclusions for files such as
.env,*.pem,*.key, credential JSON files, and sensitive directories. Keep secrets in environment variables, a vault, or an encrypted secret store rather than assistant-readable project files. Avoid opening a secret file or pasting a credential into a terminal session while an assistant with IDE or terminal context is active. - Limit agent permissions and preserve approval points. Give an agent only the tools and access needed for the task; use read-only scope where practical. Require independent approval before consequential actions, such as changing code, accessing systems, or running commands that affect data or infrastructure.
- Verify the result independently. Treat suggestions as hypotheses. Check the affected code path, versions, exploit preconditions, and impact through code review, established static or dynamic analysis, and carefully controlled tests. Review generated code and commands before running them.
Can a README or issue prompt an agent to leak secrets?
It can contain instructions intended to manipulate an agent. Repository files, pull requests, issue text, external documentation, and retrieved pages should all be treated as untrusted input: an attacker may hide malicious instructions in material the assistant reads. OWASP describes this as indirect prompt injection and states that “there is no fool-proof prevention within the LLM.” A prompt telling the assistant to ignore instructions in files is not a dependable security boundary.
Reduce the consequences rather than relying on the model to recognize every attack. Exclude secrets, restrict the agent’s access and tools, prefer read-only operation where possible, and require human approval for consequential actions. OWASP’s LLM01: Prompt Injection explains the risks of malicious instructions in external content. NIST CAISI’s January 17, 2025 article, Strengthening AI Agent Hijacking Evaluations, describes agent hijacking as indirect injection that can cause unintended harmful actions.
Should you use a local or air-gapped model for confidential code?
For classified, regulated, or highly sensitive code, OWASP recommends considering self-hosted or air-gapped coding tools. This changes the deployment boundary; it does not automatically make the workflow safe. A locally operated system still needs review of its components, software supply chain, user and agent access, logging, network connections, update process, and operational controls. Confirm that the deployment meets your organization’s requirements before use.
When comparing deployment choices, evaluate them against the same criteria rather than treating “local” or “enterprise” as a blanket assurance:
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- Whether the deployment is approved for the data classification.
- What context it can read and whether exclusions are available and effective.
- Retention, deletion, training use, data residency, and access controls.
- Agent, plugin, terminal, and repository permissions.
- Whether self-hosted or air-gapped operation is feasible and how its components and supply chain are reviewed.
- Whether the team can test prompt injection and other failure modes before use and after material changes.
How should teams evaluate an assistant before adopting it?
Assess the tool’s threat model and boundaries before onboarding it. OWASP AISVS 1.0 Appendix C, AC.2.1, states: “Verify that every AI tool, whether it is an assistant, a reviewer, an agent, or an MCP server, has a threat model.” That evaluation should cover prompt injection, training-data leakage, insecure output handling, excessive agency, and supply-chain risks, as well as the tool’s actual data flows and permissions.
Test adversarial cases relevant to your code-review workflow, including untrusted instructions in repository files and attempts to access excluded material. Repeat the evaluation when the model, integrations, permissions, or configuration changes materially. OWASP’s AI Agent Security Cheat Sheet provides guidance on least privilege, untrusted inputs, privacy, and repeatable testing. NIST CAISI likewise discusses adaptive evaluation of agent hijacking risks in its January 17, 2025 article.
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