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Freedom251 › Blog › AI security

Are AI Vulnerability Scanners Safe for Confidential Source Code? FAQ

By Freedom251 Team · · 8 min read

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AI vulnerability scanners can be safe for confidential source code only when you can verify exactly what leaves your environment, who can access it, how long it is retained, which model providers and subprocessors receive it, and how deletion is proven. A hosted scanner should be treated as receiving source code or security context unless its technical and contractual documentation says otherwise. A local or customer-managed scanner reduces third-party transfer, but it still runs with local permissions and can expose repositories through logs, plugins, credentials, model endpoints, or an improperly isolated runner.

“Not used for training” is only one answer. Before scanning proprietary code, establish the complete data flow, retention period, deletion process, regional processing, encryption, tenant isolation, access logging, and human-review rules. Keep AI output under the same controls as conventional static analysis, dependency scanning, secret scanning, and security review.

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What an AI vulnerability scanner may receive

An AI feature rarely needs only a file called source.zip. Depending on the product and configuration, it may send source files, selected snippets, file names, repository paths, abstract syntax trees, code locations, HTTP messages, dependency information, prompts, stack traces, vulnerability metadata, and the findings generated from them. Even if source text is filtered, a combination of names, routes, function signatures, and findings can reveal architecture or sensitive business logic.

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Hosted SAST or DAST correlation services commonly send selected context to model infrastructure to explain a suspected issue, prioritize findings, or generate remediation. The only reliable answer for a particular scanner is its documented data-flow diagram and the controls in your contract; a product label such as “AI-assisted” does not define the payload.

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Source code is not the only confidentiality boundary

  • Repository names, branch names, file paths, ticket IDs, and commit messages can disclose projects, customers, or unreleased features.
  • HTTP requests and responses may contain tokens, personal data, account identifiers, or proprietary API behavior.
  • Findings can expose exploitable locations even when the original code was redacted.
  • Logs, prompts, crash reports, support bundles, and model-evaluation traces may create additional copies.

Hosted, local, and hybrid execution

Choose an architecture according to the repository’s confidentiality classification, not simply the scanner’s detection claims.

Deployment What happens to data Advantages Risks and questions
Hosted SaaS Code or security context is sent to the vendor’s service and possibly its configured model providers. Fast deployment, managed scaling, and access to large models without operating inference infrastructure. Confirm every recipient, retention window, deletion evidence, region, subcontractor, tenant isolation, and support access. Vendor policy claims are not an independent audit.
Local scanner with local model Scanning and inference remain in an environment you control. Strongest control over network transfer and data residency when correctly isolated. You must secure the operating system, runner, model, plugins, dependencies, update channel, logs, backups, and inherited credentials. A local process can still read more than intended.
Hybrid scanner Scanning may be local while selected snippets, prompts, or findings go to an external model provider. Can limit exposure by sending only narrowly scoped context and retaining local control of the repository. Verify the exact redaction and allow-list behavior. “Hybrid” does not identify which files, snippets, or metadata leave the network.

OpenText Fortify explains that its AI-powered SAST passes selected source files to a configured large language model and that off-cloud users can provision their own model instance. That illustrates why deployment mode and model configuration must be evaluated together: a nominally local scanner can still be cloud-connected if its model endpoint is external.

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“Not used for training” is not a complete privacy answer

A provider’s statement that customer data is not used to train its general models addresses one purpose, not every way data can persist or be accessed. Ask separately about:

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  • Retention: Is data held only in memory, for the scan session, for a fixed number of days, or in backups and disaster-recovery systems?
  • Deletion: Can you delete data on demand, and will the provider supply an auditable confirmation covering caches, logs, and backups?
  • Access: Can vendor engineers, support staff, trust-and-safety reviewers, or other customers see prompts, source, or findings?
  • Subprocessors: Which model hosts, telemetry vendors, storage systems, and support providers receive the data?
  • Purpose limits: Is information used for abuse monitoring, quality evaluation, debugging, or human review even when it is excluded from model training?
  • Location: In which countries is data processed, stored, or backed up, and can the region be selected?

PortSwigger says it uses providers “that can guarantee your data is not used for training.” Treat that as one control to verify alongside the other questions above, not as a blanket confidentiality guarantee.

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What vendor statements establish—and what they do not

Published policies provide useful commitments, but they describe stated controls rather than an independent inspection of every deployment or tenant. Compare the wording and scope carefully.

  • OpenAI’s Codex Security policy says: “Scan only repositories you trust and either own or have explicit permission to assess.” It also says: “Keep credentials, private source, and scan results out of model requests, logs, reports, and network destinations the operator did not authorize.” These are operational requirements for the person configuring a scan, not a promise that a hosted service will never receive source code.
  • Snyk documentation says its SAST/DAST correlation can process “Customer source code” and related vulnerability metadata, while stating that “Customer proprietary software code is not retained by the provider of these AI models.” Confirm which Snyk feature, model route, region, and plan the statement covers before relying on it.
  • Checkmarx states: “Uploaded source code is stored only for the duration of the scan session.” Clarify what “scan session” includes, whether findings and logs have different retention, and how backups are handled.
  • OpenText Fortify documents selected-file transfer to a configured LLM and an off-cloud option. Determine whether your installation uses the off-cloud path, what telemetry remains, and who operates the model.

None of these statements, by themselves, proves that a particular tenant configuration is safe for your repository. Obtain the current policy, data-processing terms, security addendum, subprocessors list, and product configuration for the exact edition you will run.

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Due-diligence checklist before uploading proprietary code

  1. Classify the repository. Mark whether it contains trade secrets, regulated data, credentials, export-controlled technology, customer code, or information subject to contractual residency rules. Define which classes an AI scanner may process.
  2. Request a data-flow diagram. Require the vendor to name the scanner service, storage systems, model provider, telemetry pipeline, support tooling, subprocessors, and every region involved.
  3. Inventory the payload. Get a field-level list of files, snippets, ASTs, prompts, HTTP messages, dependency manifests, file names, paths, logs, and findings that leave the repository. Ask whether uploads are full-repository, changed-file, or finding-triggered.
  4. Set retention and deletion terms. Record the normal retention period, backup lifetime, cache behavior, deletion SLA, and the evidence supplied after deletion. Ensure findings and logs are covered, not only uploaded source.
  5. Check model-use restrictions. Confirm whether prompts, source, and findings are excluded from training, abuse monitoring, quality review, and human inspection. Identify every model provider and the contractual flow-down of those restrictions.
  6. Verify security controls. Confirm encryption in transit and at rest, key-management responsibilities, tenant isolation, vulnerability management, administrative access controls, and immutable access logs.
  7. Confirm residency and legal scope. Select an allowed processing region where possible and verify cross-border transfers, government-access terms, and the treatment of backups and support cases.
  8. Use least privilege. Give the scanner read-only access to the smallest repository set and shortest-lived credentials possible. Keep secrets, signing keys, production tokens, and personal data out of the scan.
  9. Run a sanitized pilot. Create a representative test repository containing synthetic secrets and known vulnerabilities. Compare network destinations, logs, prompts, findings, and deletion behavior with the vendor’s written claims.
  10. Document approval. Record the accepted data classes, configuration, policy version, region, retention, owner, and conditions for re-review when the vendor changes models or subprocessors.
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Hardening a local or self-hosted scanner

Self-hosting changes who operates the infrastructure; it does not eliminate risk. The scanner process, model server, extensions, and build tools may inherit the same permissions as the account that launches them.

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Isolate the execution environment

  • Run the scanner in a dedicated VM, container, or runner with a read-only checkout where feasible.
  • Deny outbound network access by default; allow only the model endpoint, package mirrors, and update services that your security team has approved.
  • Mount only the repository and configuration paths required for the scan. Do not expose home directories, cloud credentials, SSH agents, production kubeconfig files, or CI secrets.
  • Disable unneeded plugins, hooks, post-processing scripts, and automatic issue-tracker uploads.
  • Protect model weights, prompt templates, caches, and logs as confidential artifacts. Apply encryption, access controls, retention limits, and backup rules to them.

Secure the model and update path

Verify the origin and integrity of model weights, container images, scanner binaries, and plugins. Pin versions in production, review changes before rollout, and monitor for vulnerable dependencies. A local model can memorize or echo sensitive input if prompts, context windows, evaluation traces, or debugging logs are retained. Configure logging at the minimum level needed for troubleshooting and test that secrets are redacted before logs leave the runner.

Control credentials and operators

Use short-lived, read-only repository tokens and separate service accounts. Restrict who can change the model endpoint, scanner policy, network rules, and result destinations. Log administrative actions and scanner access, then alert on unexpected repository reads or outbound connections.

Make AI findings part of—not a replacement for—security controls

AI-assisted analysis can explain a suspicious flow, suggest a fix, or correlate SAST and DAST evidence. It can also hallucinate a vulnerability, miss a path, misunderstand generated code, or recommend an unsafe change. Treat its output as a hypothesis that requires normal verification.

  • Run established SAST rules, dependency and license checks, secret scanning, and infrastructure checks alongside the AI feature.
  • Require a developer or security engineer to inspect the data flow, exploitability, and proposed remediation before merging.
  • Keep reproducible evidence: the rule, file and line, input, model version, prompt policy, and reviewer decision.
  • Do not paste credentials, private keys, customer records, or unredacted production traffic into a model request to improve an explanation.
  • Re-test after upgrades to the scanner, model, prompt templates, region, or subprocessors because behavior and data handling can change.

Warning signs that should stop a deployment

  • The vendor cannot name all model providers or subprocessors receiving code or findings.
  • Retention is described only as “temporary” or “until processing is complete,” with no measurable period or deletion evidence.
  • The contract excludes prompts, logs, support bundles, backups, or generated findings from its confidentiality terms.
  • Region selection, tenant isolation, encryption details, or administrative access logs are unavailable for your edition.
  • The scanner requests broad write access, persistent administrator credentials, or unrestricted network egress.
  • There is no way to disable telemetry, human review, training-related use, or automatic issue and report uploads.
  • The product’s marketing says “private” or “self-hosted” while the configured model endpoint is a third-party cloud service.

A practical decision rule

Use a hosted AI scanner for confidential code only when its documented data flow, contract, retention, deletion, model-provider terms, region, and access controls meet your organization’s classification requirements and a controlled pilot confirms the configuration. Choose a local or customer-managed model when transfer to a provider is unacceptable and your team can operate the isolation, patching, credential, logging, and model-governance workload. A hybrid design is reasonable when selected context can be tightly allow-listed and redacted, but verify that the controls actually constrain every payload.

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If any material answer is unknown, do not upload the repository. Run conventional local checks, sanitize a test copy, or obtain a written exception from the owner of the source and your security or privacy team.

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