# Trail of Bits Canonical page: https://aiteamrecord.com/company/trail-of-bits/ > Human-led AI/ML security assessments across models, pipelines, agents and their supporting systems. ## What this provider does Trail of Bits offers AI and machine-learning security assessments spanning training data, MLOps pipelines, model artifacts, inference infrastructure and deployed agents. Its official service page describes threat modeling, adversarial testing, capability evaluation and engineering handover artifacts such as findings, proof-of-concept code and CI-ready rules. These service and deliverable descriptions are provider-reported; the scope of a particular engagement needs confirmation. ### What the team assesses Trail of Bits describes a human-led security practice for AI and machine-learning systems. Its AI/ML service page spans training data, MLOps pipelines, model artifacts, inference hardware and deployed agent loops. The offer is a technical assessment of a system and its surrounding infrastructure, not a subscription to a monitoring platform or a promise to build an AI product for the buyer. The practice says it combines machine-learning, application-security, systems and cryptography specialists on engagements. This matters where a failure crosses boundaries, for example between model behavior and tool permissions. The page does not establish that every component or discipline is examined at identical depth in every project. Sources: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### From threat model to adversarial test The published method begins by defining the system boundary, adversary and operational design domain. It then reviews pipeline and artifact risks such as data provenance, CI/CD and model serialization before probing models and agents for behavior including prompt injection, data exfiltration and capability misuse. Root-cause analysis follows the findings so the report can address the design assumption behind a weakness. Trail of Bits also lists AI risk assessments, model-capability evaluations and tailored security training. Those are distinct scopes: architecture and policy review, measured model behavior, and staff learning should not be confused with one universal penetration-test package. A buyer should specify which assets, source access and testing conditions are authorized. Sources: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### What engineering teams receive The service page lists a written findings report with severity and exploit scenarios, runnable proof-of-concept or code artifacts, and short- and long-term software-development recommendations. It also describes CI-ready static-analysis rules, fuzzing or evaluation harnesses, a live walkthrough and retesting of fixes. These are the provider’s stated deliverables, not proof that a particular customer received every item. For a smaller engineering team, the practical question is which artifacts will be usable in its own repository and pipeline after the assessment. Confirm the exact rule formats, ownership of test artifacts, access to sensitive results and retest window in the statement of work. No fixed price, timeline, service country or independently verified outcome was established by the reviewed pages. Sources: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### Buyer fit Organizations building or deploying AI models, agents or ML pipelines that need a scoped technical security review. Agree on assets, threat model, testing access, deliverables, retest and handling of sensitive artifacts before work starts; published material does not establish a price or universal geographic coverage. ## Services and focus - Provider type: AI consultancy - Delivery mode: AI Security Assessment - Tasks: [Assess AI system security](https://aiteamrecord.com/tasks/assess-ai-system-security/) - Industries: [Cybersecurity](https://aiteamrecord.com/industries/cybersecurity/) ## Claims and sources A listing is not an endorsement or a guarantee of delivery. Provider-reported statements are not independent verification. Not stated means unknown, not absent. Source review dates are distinct from independent verification dates. Owner claim status concerns profile control, not service delivery. ### AI/ML assessment scope Statement: Describes security review of training data, MLOps pipelines, model artifacts, inference hardware and deployed agent loops. Status: Provider-reported - Source: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### Assessment sequence Statement: Describes scope and threat model, pipeline/artifact review, adversarial testing and capability evaluation, root-cause analysis, then reporting and remediation. Status: Provider-reported - Source: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### Engineering handover Statement: Lists written findings, proof-of-concept artifacts, CI-ready Semgrep or CodeQL rules, evaluation harnesses, walkthrough and fix-review retesting among engagement deliverables. Status: Provider-reported - Source: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ### Multidisciplinary team Statement: Says AI/ML engagements involve ML, application-security, systems and cryptography specialists. Status: Provider-reported - Source: [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) ## Service details - Service coverage: Not stated - Headquarters: Not stated - Languages: Not stated - Pricing: Not stated - Official website: [Trail of Bits](https://trailofbits.com/) ## Profile sources and updates This profile has not been independently verified. Sources last reviewed: 2026-10-11 Content last updated: 2026-10-11T10:58:14.540Z Owner claim status: Not claimed ### Original sources - [Trail of Bits — AI/ML Security](https://trailofbits.com/services/ai-ml/) — Provider source; checked 2026-10-11 - [Trail of Bits — official website](https://trailofbits.com/) — Provider source; checked 2026-10-11 Corrections: https://aiteamrecord.com/correction?provider=trail-of-bits Interpretation guide: https://aiteamrecord.com/methodology.md