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No. AI can discover software and model weaknesses faster and at much greater scale, but it cannot eliminate vulnerabilities that vendors fail to disclose, prioritize, patch, or communicate. The practical change is a shorter period in which a flaw remains unknown—not the end of hidden risk. Buyers should judge an AI vendor by measurable controls: a public disclosure route, accountable incident response, software and AI supply-chain inventories, secure development, repeated model testing, and independent validation.
Why won’t AI automatically end hidden vulnerabilities?
AI-assisted code analysis, fuzzing, anomaly detection, and model testing can examine more paths than a small human team. That speed helps researchers and attackers find defects earlier. It does not ensure that a vendor knows which component is affected, can reproduce the issue, has an owner for remediation, or tells customers what to do.
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CISA’s review of fiscal years 2024 and 2025, published August 26, 2026, is intended to establish “a baseline of today’s vulnerability landscape before AI-enabled vulnerability discovery becomes more widespread.” Its baseline matters because attackers still exploit exposed, well-known weaknesses. Faster discovery therefore increases the value of basic patching and disclosure discipline rather than replacing them.
Hidden vulnerability risk has several parts:
- Discovery lag: nobody has identified the defect or its exploit path.
- Recognition lag: a researcher has reported it, but the vendor has not confirmed or triaged it.
- Remediation lag: a fix, configuration change, or compensating control is still being developed.
- Customer visibility lag: users cannot tell whether their deployed version, model, dependency, or data is affected.
AI primarily compresses the first interval. A vendor’s processes determine the other three.
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Are AI vendors ready for hidden vulnerabilities?
Readiness is uneven. A 2025 study titled “Measuring the Vulnerability Disclosure Policies of AI Vendors” found that 36% of the vendors it examined had no disclosure channel, while only 18% explicitly mentioned AI-related risks. Those figures describe that study’s sample, not every AI company, but they show why marketing claims about secure AI are not a readiness test.
A ready vendor makes its security work observable before an incident. Look for the following evidence:
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- A public vulnerability-disclosure policy that states scope, reporting methods, testing limits, response expectations, and safe-harbor terms.
- A Product Security Incident Response Team (PSIRT), or a named equivalent with authority to coordinate engineering, legal, communications, and customer support.
- Coordinated vulnerability disclosure (CVD) practices aligned with ISO/IEC 29147 and ISO/IEC 30111 where those standards fit the product and geography.
- Software bills of materials (SBOMs) that cover applications, containers, models, data-processing libraries, and build-time dependencies, plus AI-specific elements when applicable.
- Vulnerability Exploitability eXchange (VEX) statements, or another machine-readable explanation of whether a listed flaw affects the shipped product.
- Secure-development evidence, independent testing, red-team reports or summaries, and a documented remediation workflow.
- Supplier and open-source controls that include undocumented or poorly secured model components.
How do vendors disclose AI security flaws?
Disclosure should give a researcher a safe, predictable path from report to resolution and give customers enough technical information to make a decision.
1. Start with a usable public policy
NIST guidance says acquiring entities should require suppliers to maintain “a formal, publicly available means by which the public can notify the supplier of vulnerabilities.” A policy should identify an email address or portal, supported encryption, in-scope products and domains, prohibited tests, evidence requirements, acknowledgement targets, and how reporters can track a case.
CISA and its partners describe a robust policy as one that specifies how researchers may look for and report vulnerabilities, which systems they may test, permitted tests, and safe-harbor language. A vague “contact support” instruction is not equivalent: it can leave researchers unsure whether testing is authorized or whether a report will reach security staff.
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2. Coordinate rather than publish prematurely
Under CVD, the vendor validates the issue, works with the reporter on a disclosure date, develops a fix or mitigation, and publishes an advisory. The advisory should identify affected versions, severity rationale, exploitation status, workarounds, fixed versions, and credit where the reporter agrees. If exploitation is active, the vendor should coordinate with relevant national or sector authorities instead of waiting for a perfect patch.
3. Make the advisory machine-readable
Human-readable notices are necessary but difficult to process across large fleets. VEX data can state that a component is affected, not affected, affected but not exploitable, or under investigation. That distinction prevents customers from either ignoring a real issue or interrupting systems for a library flaw that cannot be reached in the vendor’s configuration.
What should an AI vendor provide in an SBOM?
An AI software bill of materials should let a customer map a vulnerability to the exact artifact in use, not merely list a product name. For conventional software, that means component names, versions, suppliers, identifiers such as package URLs or common platform enumeration references, dependency relationships, and the build or release that contains them.
AI deployments add layers that ordinary application SBOMs may omit. Request, where relevant:
- Model name, version, hash or digest, format, and provenance.
- Base model, fine-tuning checkpoints, adapters, embeddings, tokenizer files, and inference runtime.
- Training and evaluation libraries, GPU or accelerator drivers, operating-system images, and container digests.
- External models, plugins, tools, agents, retrieval connectors, and data-processing packages.
- Known licensing, usage restrictions, and whether a component is fetched dynamically at runtime.
- Release date, support status, and the vendor’s process for updating or revoking a compromised artifact.
Ask how the vendor updates the SBOM when a model is retrained, a dependency is rebuilt, or an API silently changes. A static document delivered once a year cannot describe a continuously changing AI service.
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How do UK and US guidance define AI lifecycle security?
The UK Department for Science, Innovation and Technology’s 2024 AI cyber-risk assessment (evidence cutoff: February 10, 2024) maps vulnerabilities across the AI lifecycle rather than treating the deployed model as the only asset. Risks can enter during data collection, training, fine-tuning, packaging, deployment, monitoring, and retirement.
NIST’s Secure Software Development Framework companion for generative AI, SP 800-218A, released July 26, 2024, extends secure-development practices to AI-specific threats. It is most useful when a vendor can show how requirements, threat modeling, testing, and release approvals change for model and data risks.
Joint guidance from CISA, NSA, the FBI, ASD ACSC, CCCS, NCSC-NZ, and NCSC-UK sets the operational aim to “Improve the confidentiality, integrity, and availability of AI systems” and ensure mitigations for known vulnerabilities. Translate that into controls at each stage:
| Lifecycle stage | Questions a buyer should ask | Evidence to request |
|---|---|---|
| Data and training | How are poisoning, unauthorized data changes, and sensitive-data leakage detected? | Data provenance rules, access logs, validation tests, and incident procedures. |
| Model and dependency build | Who approves base models, packages, and downloaded artifacts? | SBOM, hashes, signed builds, dependency scanning, and supplier reviews. |
| Evaluation | Are security tests repeated after material model or prompt changes? | Evaluation plan, regression results, red-team scope, and remediation tickets. |
| Deployment | How are secrets, tools, plugins, and tenant boundaries protected? | Architecture diagrams, least-privilege settings, isolation tests, and monitoring coverage. |
| Operations | How quickly can the vendor detect abuse or a newly disclosed component flaw? | Detection objectives, on-call ownership, playbooks, and customer notification criteria. |
| Retirement | How are old models, logs, keys, and replicas removed? | Decommissioning records, retention rules, revocation and deletion procedures. |
Can AI find vulnerabilities faster than vendors can fix them?
Yes. Discovery can be automated across repositories, dependencies, APIs, prompts, and model behavior while remediation still requires reproduction, prioritization, engineering changes, regression testing, and customer deployment. The resulting gap is a race between finding evidence and reducing exposure.
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- Time to acknowledge a report.
- Time to reproduce and assign an owner.
- Time to publish an initial impact statement.
- Time to provide a mitigation or fixed release.
- Time for customers to receive an advisory, updated SBOM, and VEX status.
- Percentage of fixes that pass regression and independent verification.
Ask whether targets differ for actively exploited flaws, critical internet-facing defects, model-integrity attacks, and ordinary dependency issues. A vendor that publishes only a mean or best-case figure can hide long-tail cases.
How should buyers compare AI vendors?
Use the same questions for every supplier and score evidence, not promises. The following comparison axes expose operational differences that product demonstrations rarely show.
| Axis | Strong signal | Warning sign |
|---|---|---|
| Disclosure accessibility | Public policy, dedicated channel, encryption option, case tracking | Only a generic support form or no published process |
| Safe-harbor scope | Clear authorized testing boundaries and protection for good-faith research | Unqualified threats of legal action or no testing rules |
| Acknowledgement and remediation | Published targets by severity, owner, and customer update cadence | “We address issues promptly” without measurable commitments |
| PSIRT ownership | Named team, escalation path, and incident communications role | Responsibility dispersed across sales or ordinary support |
| SBOM and VEX depth | Versioned software and AI inventories with exploitability status | Marketing-level component list with no update process |
| Third-party testing | Recent independent penetration tests, red-team scope, and remediation summary | Undated “audited” claim with no scope or exceptions |
| Model-change evaluation | Security regression tests rerun after material model, data, or prompt changes | One prelaunch assessment for a continuously changing service |
| Supplier controls | Risk-tiering, contract requirements, provenance, and open-source monitoring | No inventory of external models, plugins, or build services |
| Incident communication | Version-specific notices, customer actions, status updates, and post-incident review | Silence until a third party publishes an exploit |
What security questions should I ask an AI supplier?
Put the answers in procurement records and, for high-impact systems, in the contract. Ask these questions before granting production access:
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- Where is your vulnerability-disclosure policy, and what systems may an independent researcher test?
- Who owns a report from intake through remediation, and what are your acknowledgement and update targets?
- Will you provide an SBOM covering application, container, model, runtime, plugin, and dynamically fetched components?
- How will you notify us when a dependency, base model, or training artifact changes?
- Do you publish VEX or equivalent exploitability statements, and how quickly are they updated?
- What independent security testing and AI red-team assessment have you completed, what was in scope, and which high-risk findings remain open?
- Which evaluations run again after material model, data, prompt, or tool changes?
- How do you assess suppliers and open-source components that lack a security track record?
- What evidence shows that tenant isolation, secrets handling, logging, and rollback work as designed?
- What customer notification, evidence-preservation, and assistance commitments apply if your service is compromised?
What can a vendor do now to shorten its hidden-vulnerability window?
Vendors do not need a perfect AI-security program to make measurable progress. Sequence the work so that researchers and customers can report and act on risk immediately.
- Assign ownership. Create a PSIRT or name an equivalent accountable team, with an executive escalation path.
- Publish the rules. Release a disclosure policy with scope, safe harbor, permitted tests, contact methods, and response targets.
- Inventory what ships. Generate versioned SBOMs for software and AI components; record model lineage and hashes.
- Add exploitability context. Produce VEX statements and explain compensating controls when a vulnerable component is unreachable.
- Integrate secure development. Apply threat modeling, code and dependency analysis, signed builds, secrets protection, and release gates.
- Test behavior as well as code. Run abuse-case evaluations, adversarial testing, and independent red teaming; repeat them after material changes.
- Exercise response. Simulate a dependency zero-day, model poisoning event, and exposed credential. Measure acknowledgement, containment, customer notification, and recovery.
- Close the feedback loop. Publish advisories, update inventories, credit researchers when appropriate, and document corrective actions.
What are the most common signs that a vendor is not ready?
- The only security contact is a sales or general support address.
- The vendor refuses authorized testing but provides no alternative assessment evidence.
- Security documentation names a framework without identifying controls, owners, dates, or exceptions.
- SBOMs omit model files, containers, plugins, or transitive dependencies.
- Model updates occur frequently, but evaluation and change-notification practices are not documented.
- Advisories list a CVE yet do not state affected versions, exploitability, mitigation, or customer action.
- Third-party test reports are undated, redacted beyond usefulness, or exclude the interfaces customers actually use.
- Supplier risk stops at first-tier software and ignores external models, data providers, agents, and open-source maintainers.
None of these signs proves that a product is exploitable. They show that a buyer cannot reliably discover, prioritize, or contain a problem when conditions change.
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What does “ready” mean in practice?
Readiness means a vendor can turn a researcher’s signal into a controlled customer outcome: acknowledge the report, reproduce it, identify affected artifacts, mitigate exposure, issue a precise advisory, and verify the fix. It also means the vendor can explain what changed in a model or dependency and rerun security evaluations instead of treating every release as identical.
AI will likely make hidden vulnerabilities harder to keep hidden for long. Vendors that pair that speed with disclosure, supply-chain visibility, lifecycle security, and independent testing can reduce harm. Vendors that rely on discovery tools without those processes may simply learn about more flaws faster than they can manage them.
Frequently Asked Questions
How often should an AI vendor update its SBOM?
It should update the inventory whenever a shipped software, model, container, plugin, or runtime component changes, and provide a version history so customers can map changes to deployments. A fixed annual cadence is inadequate for a continuously updated service.
Should a customer accept a vendor that has no bug-bounty program?
A bug bounty is useful but not mandatory. A vendor can demonstrate readiness through an accessible disclosure policy, safe-harbor terms, coordinated handling, independent testing, and measurable remediation even if it uses an invitation-only or researcher-contract model.
What should a contract say about AI vulnerability incidents?
Specify notification deadlines, the technical details the vendor must provide, evidence-preservation duties, cooperation with customer investigation, remediation commitments, and how service credits or termination work when risk is not contained. Tie obligations to affected versions and customer actions rather than promising that no vulnerability will occur.
How can a small AI supplier prove security without a large PSIRT?
Name a trained incident owner and backup, use an external response provider when needed, publish the same disclosure and safe-harbor information as a larger vendor, and maintain versioned inventories and test records. Clear ownership and evidence matter more than the size of the team.
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Does a VEX statement replace patching?
No. VEX explains whether a known component vulnerability is exploitable in a particular product and configuration. If reachability or compensating controls change, the vendor must reassess, issue an update or mitigation, and revise the VEX status.
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