Best AI Security Consultants (2026): 7 Firms to Hire

Your team now ships code that an AI agent wrote, and the agent can read your .env file, run shell commands and call MCP servers. A classic pentest checks your APIs and login flows. It does not check what Claude Code or Cursor is allowed to touch on a developer laptop, or whether a poisoned README can steer your support chatbot.

That gap is why AI security consultants exist as their own category. This list covers 7 firms you can hire for AI, LLM and Claude Code security in 2026. Each entry says what the firm tests, how big it is, where it is based and what it charges, taken from the firm's own pages on 9 November 2026. Where a firm publishes no price, we say so.

Best AI security consultants: a brief overview

• AY Automate: Best for Claude Code and AI coding agent security. It audits what your agents can read, run and send in 1 to 2 weeks, then hardens the setup.
• Trail of Bits: Best for deep assurance of models and ML pipelines, from training data to inference hardware.
• Bishop Fox: Best for full-stack AI penetration testing that chains model, agent, app and cloud findings.
• NetSPI: Best for continuous AI pentesting inside a PTaaS platform, including validation of findings from AI security tools.
• Cobalt: Best place to hire vetted pentesters on demand for an AI or LLM app, paid with prepaid credits.
• Software Secured: Best for a published starting price on an AI pentest.
• Doyensec: Best boutique for code-level review of LLM features, sandboxes and tenant isolation.

1. AY Automate, best for Claude Code and AI coding agent security

AY Automate runs a focused Claude Code security audit for teams that rolled AI coding agents out across their engineers. The audit maps what Claude Code, Copilot, Cursor and your MCP servers can read, run and send: files and secrets, shell commands, network egress, MCP OAuth tokens and prompt injection paths. Then the team fixes what it finds.

The audit checks your setup against publicly disclosed risk classes, including hook-based command injection (CVE-2025-59536) and environment variable exfiltration (CVE-2026-21852), and maps findings to the OWASP Top 10 for Agentic Applications 2026. AY Automate is a formal Claude partner and builds on Claude Code every day, so the hardening comes from people who run the same setup in production. It has shipped for 60+ clients. In its published AmbryHill case study, an aerospace ERP company under FAA, ITAR and AS9100 rules, AY turned the whole database into an agent where each person only sees and reports on what they are allowed to touch.

Why it's on the list

• AI-native engineers embedded in your team audit and harden what Claude Code, Cursor, Copilot and your MCP servers can read, run and send, and ship the fixes to production.
• "Live in 10 days, not 10 weeks." The engineer joins your Slack and repo and ships the first working automation in week two (how we work).
• Monthly retainer: most teams stay with us on a monthly retainer after launch (FAQ). Engineers start at $3K a month, with a 90-day replacement guarantee.
• Proof: the compliance automation case study, an AI document-verification system that eliminated €10K+ in regulatory fines and cut admin time 70%.

Key features

• Audit (3 to 5 days): CLAUDE.md, settings.json, hooks, .claudeignore scope, MCP server inventory and token storage
• Harden (5 to 7 days): command allowlists, approval gates for destructive commands, MCP scoping, PreToolUse and PostToolUse hook checks, SAST in CI for AI-written pull requests
• Half-day developer training on prompt injection, context hygiene and safe .env handling
• Optional monthly retainer: configuration drift review, policy alerts, incident response drills
• Covers Claude Code, GitHub Copilot, Cursor, MCP servers and custom coding agents

Pricing

• Free 30-minute call where AY names your top exposure points, not behind a paid report
• Audit priced by team size and number of MCP servers, with a written quote before any paid work
• Need engineers who stay? An AI engineer on your team from $3K a month, cancel anytime, with a 90-day replacement guarantee

Pros

• A team of up to 20 developers gets a full audit and report in 1 to 2 weeks
• You leave with working files: a hardened .claudeignore, an MCP trust register, an allowlist policy and a runbook
• The same team builds agents, so fixes ship as version-controlled config changes your engineers review, while they keep shipping

Best for

• Engineering leads who rolled out Claude Code, Cursor or Copilot and want proof of what those agents can reach, audited in 1 to 2 weeks and hardened the week after, by a Claude partner that has shipped for 60+ clients and puts engineers on your team from $3K a month with a 90-day replacement guarantee

Meet our Claude Code development agency or hire AI developers from our own bench.

2. Trail of Bits, best for deep model and ML pipeline assurance

Trail of Bits assesses AI/ML systems end to end: training data, MLOps pipelines, model artifacts, inference hardware and deployed agent loops. It staffs engagements across ML, application security, systems and cryptography, because many AI failures sit between those disciplines.

Its public reviews include SafeTensors (March 2023, 2 weeks) and YOLOv7 (October 2023, 4 weeks). It also cites its work building AI systems that find and patch bugs for DARPA's AI Cyber Challenge.

Why it's on the list

• Assesses AI/ML systems end to end: training data, MLOps pipelines, model artifacts, inference hardware and agent loops.
• Fits teams that train or serve their own models; 50 to 200 people, New York.
• Pricing not published; project engagements quoted on request, with effort listed in weeks on its public reviews.
• Proof: its engagement table lists a Sep 2026 OpenAI OpenClaw AI/ML review (3 weeks) and a Sep 2026 Google Private AI Compute review (5 weeks) (Trail of Bits AI/ML page).

Key features

• ML-Ops and pipeline assessment, including CI/CD, data provenance and GPU stack review
• AI risk assessment with threat modeling and operational design domain analysis
• Model capability evaluation against expert baselines
• Deliverables include exploit PoCs, custom Semgrep and CodeQL rules and a fix-review retest

Pricing

• Not published

Pros

• Leaves you tooling to keep running: static-analysis rules and red-team harnesses
• Publishes generalizable findings as public research

Cons

• Built for model builders and ML platforms; a team that only uses Claude Code may not need pipeline-level review
• Lists no starting price

Team and location: 50 to 200 people, New York, founded 2012 (company data via looot enrichment).

Best for

• Teams training or serving their own models who need the pipeline, artifacts and hardware reviewed

3. Bishop Fox, best for full-stack AI penetration testing

Bishop Fox offers AI/LLM pen testing and a separate GenAI architecture security review. The pentest goes after the model, the app it sits in, the retrieval pipeline, agent permissions and the APIs it calls, then links findings across layers. One example from its page: a model weakness, plus an agent permission issue, plus an exposed cloud credential.

Testing aligns to the OWASP Top 10 for LLM Applications, Machine Learning and Agentic Applications, and MITRE ATLAS. Reports come with a walkthrough and a retest.

Why it's on the list

• Pentests the model, the app around it, the retrieval pipeline, agent permissions and the APIs it calls, then chains findings across layers.
• Fits companies with a customer-facing LLM app or agent; 200 to 500 people, Tempe, Arizona.
• Pricing not published; project-based engagements.
• Proof: Ventrilo.ai, an AI writing assistant, had Bishop Fox run application pentesting and AI security assessments before launch (Bishop Fox customer story).

Key features

• Automated multi-turn jailbreak testing combined with human-led testing
• Separate architecture review covering policy, AI usage discovery and interviews
• Staging-first, rate-limited testing to avoid production disruption
• Ongoing triage for clients whose AI footprint keeps growing

Pricing

• Not published

Pros

• Clear split between pentesting and red teaming, so you buy the right one
• Cross-layer attack chains show the business impact of each finding

Cons

• Scoped as a security test of your AI app; hardening the coding agents your developers use is outside the published scope
• No public starting price

Team and location: 200 to 500 people, Tempe, Arizona, founded 2005 (looot enrichment).

Best for

• Companies with a customer-facing LLM app or agent that need a pentest report for customers or auditors

4. NetSPI, best for continuous AI pentesting on a platform

NetSPI sells AI/LLM pentesting as part of its PTaaS platform. It tests LLM features in web apps on any model (GPT, Llama, Mistral, Titan) and any host (Azure, OpenAI, GCP, Bedrock). It also runs benchmarking and jailbreak work, plus custom model testing that covers model extraction, inversion and evasion.

Two services stand out: continuous AI penetration testing as your models change, and continuous AI findings validation. With the second, NetSPI experts reproduce findings from your AI security tools and filter out false positives.

Why it's on the list

• Sells AI/LLM pentesting inside its PTaaS platform, including continuous testing and validation of findings from AI security tools.
• Fits security teams that already run a pentest program; 200 to 500 people, Minneapolis.
• Pricing not published; platform subscription and project testing.
• Proof: it built a standardized AI security testing framework with Microsoft for their 70+ products (NetSPI customer story).

Key features

• LLM web application pentesting
• AI/LLM benchmarking and jailbreaking
• Custom testing that reviews training data sources and pipelines
• Findings delivered through the NetSPI platform

Pricing

• Not published

Pros

• One client quote on its page describes AI testing folded into about 70 product tests over two years
• Useful if you already buy AI scanners and need a human to sort real issues from noise

Cons

• Platform-first model fits security teams with a running pentest program more than a startup on its first test
• No public price

Team and location: 200 to 500 people, Minneapolis, founded 2001 (looot enrichment).

Best for

• Security teams that want AI testing on the same platform and cadence as their other pentests

5. Cobalt, best place to hire vetted pentesters on demand

Cobalt works like a talent platform for pentesters. Its AI and LLM pentests are run by the Cobalt Core, its community of vetted testers, using a methodology based on the OWASP Top 10 for LLM Applications and three years of AI testing. For apps that retrieve internal data, testers also probe the RAG pipeline, document injection, embeddings and access controls on retrieved content.

Its pricing page explains the credit model. One Cobalt credit equals 8 hours of testing. Credits come in annual packages, do not roll over, and include unlimited retesting during the contract.

Why it's on the list

• Runs AI and LLM pentests through its community of vetted testers, including RAG and agentic workflow testing.
• Fits product teams that launch AI features often; 200 to 500 people, San Francisco.
• Pricing: credit-based annual packages, 1 credit = 8 testing hours; the price per credit is not published.
• Proof: PowerSchool's CISO says Cobalt was the first pentest vendor to present a clear methodology for AI and LLM applications (Cobalt LLM pentest page).

Key features

• Human-led testing across the full OWASP LLM Top 10
• RAG and agentic workflow testing
• Credits you can spend on any asset when a launch comes up
• Separate autonomous pentest, promoted at $3,500 per test if started and finished before 31 December 2026

Pricing

• Credit-based annual packages; the price per credit is not published

Pros

• You can start a test in hours instead of waiting weeks for a scoping cycle
• Sample critical finding on its page shows the report depth (CVSS score, CWE, OWASP LLM mapping, fix steps)

Cons

• Unused credits expire at the end of each contract year
• The published AI scope is your application, not the coding agents your developers run

Team and location: 200 to 500 people, San Francisco, founded 2013 (looot enrichment).

Best for

• Product teams that launch AI features often and want testers on call without a new contract each time

6. Software Secured, best for a published AI pentest price

Software Secured is one of the few firms on this list that prints prices. AI pentesting starts at $10,800 USD, done by full-time AI security testers, with 3 retest rounds over 12 months. Scope covers prompt injection, data leakage and model abuse.

The same page lists threat modeling from $12,400 and secure code review from $9,300, so you can budget a full AI security pass before the first call.

Why it's on the list

• Does manual AI pentesting by full-time testers, covering prompt injection, data leakage and model abuse, plus threat modeling and code review.
• Fits SaaS companies and SMBs that need a pentest report for enterprise deals; based in Ottawa.
• Pricing: AI pentesting from $10,800 USD, threat modeling from $12,400 USD.
• Proof: Cordial Systems' CEO says Software Secured proved its custody technology delivers on its security promise (Software Secured pricing page).

Key features

• Manual AI pentests scoped by models, APIs and integrations
• STRIDE-based threat modeling workshops
• PoCs and fixes exported to Jira or Azure DevOps on web and API tests
• PTaaS subscription from $21,400

Pricing

• AI pentesting from $10,800 USD
• Threat modeling from $12,400 USD

Pros

• You know the floor price before any sales call
• Retests are included for a year

Cons

• The AI floor is $10,800, with no smaller AI tier listed
• Agentic coding tools and MCP servers are not named in the published AI scope

Location: Ottawa, Canada, founded 2009 (looot enrichment).

Best for

• SaaS companies that need an AI pentest report for an enterprise deal and want a known budget

7. Doyensec, best boutique for code-level LLM review

Doyensec is a small offensive security firm with offices in San Francisco and San Marino. Its LLM and AI testing combines classic application security with LLM-specific techniques, built on the OWASP API Security Top 10 and the OWASP Machine Learning Security Top 10.

It goes after the places where LLM features meet real systems: sandboxed code execution, internal API access, request forgery and tenant isolation. It also checks that classic bugs like XSS and SQL injection have not slipped back in through the chatbot.

Why it's on the list

• Tests LLM features and chatbots with manual code review, including sandboxed code execution, internal API access and tenant isolation.
• Fits teams whose LLM feature runs code or calls internal APIs; 10 to 50 people, San Francisco.
• Pricing not published; boutique project engagements.
• Proof: two Doyensec researchers spent 60 person-days auditing the open-source Outline wiki and published the write-up (Doyensec blog).

Key features

• Prompt injection and jailbreak testing of chatbots and GenAI features
• Sandbox escape testing for code execution features
• Access control and cross-tenant data checks
• Manual source code auditing alongside dynamic testing

Pricing

• Not published

Pros

• Senior engineers who read large codebases by hand
• Treats the LLM as one part of the app, so old bug classes are still covered

Cons

• A boutique engagement, with no on-demand tester pool or platform
• No public price

Team and location: 10 to 50 people, San Francisco, founded 2017 (looot enrichment; the founding year matches the copyright line on its site).

Best for

• Teams whose LLM feature runs code or calls internal APIs and needs a careful code-level review

How we ranked these AI security consultants

We started from four searches: "best places to hire security consultants", "hire security consultant", "AI security consultants" and "LLM security audit firms". The first two return guard companies and job boards, so we focused on firms with a published AI or LLM security service.

We left out anyone ranking in Google's top 10 for "AI security consultants" and the large consultancies, which already get plenty of attention. HackerOne publishes a strong AI red teaming offer, but its company data puts it at 1,000+ employees, so it sits outside a mid-market list.

For each firm we read its own service page and pricing page, and pulled size, HQ and founding year from company enrichment data through looot. The order reflects three things: how directly the firm covers the AI risk most teams face in 2026 (agents with real access), whether it publishes scope and price, and whether it has shipped public work you can check. AY Automate leads because it is the only firm here whose published service covers the coding agents your engineers run every day, and it fixes the setup in the same engagement.

How to choose the right AI security consultant

1) Is your risk in the app or in the agents your team uses?

An LLM feature your customers use and an AI coding agent your developers use are different attack surfaces.

• If customers talk to your chatbot or agent: hire a pentest firm such as Bishop Fox, Cobalt or Software Secured.
• If your engineers run Claude Code, Cursor or MCP servers with shell and repo access: start with the AY Automate audit above. If you are also building agents that act on business systems, our AI agent development team ships them with these controls in place. Our write-up of 7 Claude Code security risks shows what that audit looks for.

2) Do you need a report or a fixed setup?

A pentest report lists findings. Someone still has to fix them.

• If an enterprise buyer wants a third-party report: Software Secured, NetSPI or Bishop Fox.
• If you want the fix shipped too: AY Automate hardens the config and can keep an engineer on your team. For self-hosted agent stacks, see our OpenClaw and NemoClaw enterprise setup.

3) Are you building models or using them?

• If you train or serve your own models: Trail of Bits or NetSPI's custom model testing.
• If you call hosted models from your app: an app-layer test plus an agent configuration audit covers most of the risk. Read our MCP security checklist before you connect another server.

4) How will you pay?

• Fixed budget and a known floor: Software Secured publishes prices.
• Many launches across the year: Cobalt's credits.
• Ongoing engineering capacity: AY Automate engineers from $3K a month, cancel anytime. See AI staff augmentation.

Run one scoped engagement first, on your riskiest agent or feature, and judge the firm on the quality of its findings before you sign a year.

FAQ

What does an AI security consultant do?

An AI security consultant tests and hardens systems that use language models. That covers prompt injection, data leakage, tool and agent permissions, RAG pipelines and the configuration of AI coding agents. Some firms only test and report; others, like AY Automate, also fix the setup.

Where is the best place to hire AI security consultants?

For Claude Code and AI coding agent security, AY Automate: a 1 to 2 week audit with hardening, from a formal Claude partner. For a pentest of a customer-facing LLM app, Bishop Fox or Cobalt. For model and ML pipeline assurance, Trail of Bits.

How much does an AI security audit cost?

Most firms on this list do not publish prices. Software Secured lists AI pentesting from $10,800. AY Automate quotes the Claude Code audit by team size and number of MCP servers on a free 30-minute call, before any paid work.

What is the difference between an LLM pentest and AI red teaming?

A pentest looks for exploitable weaknesses in the software, APIs, permissions and infrastructure around a model. Red teaming tests how the model behaves under adversarial pressure, such as jailbreaks and unsafe outputs. Many AI systems need both.

Is Claude Code a security risk?

With default settings, Claude Code can read any file the developer account can reach, run shell commands and send context to the model provider. With a scoped .claudeignore, command allowlists and approval gates, those risks are manageable. Our AI agent security best practices guide lists the controls.

How long does an LLM security audit take?

It depends on scope. AY Automate's audit for a team of up to 20 developers runs 1 to 2 weeks, with hardening adding about a week. Bishop Fox notes that a single chatbot takes less time than a full-stack review covering model, agents and infrastructure.

Do I need a security consultant if I use AI security tools?

Tools catch known patterns at scale but miss chained attacks and produce false positives. NetSPI sells human validation of AI tool findings for this reason. For a list of the tools themselves, see our best AI agent security tools roundup.