Top LLM Penetration Testing Companies in 2025
1. Cybri
Cybri delivers a PTaaS model that caters to industries adopting chatbots and AI assistants in regulated industries, while also providing comprehensive LLM attack coverage aligned to OWASP and NIST standards.
They provide end-to-end testing, as they examine the model itself, any retrieval or tool plugins connected to it, the data pipelines feeding it, and even the surrounding application logic.
Cybri stresses mapping tests to the OWASP LLM Top 10 risks, meaning Cybri’s team will execute prompt injections, output tampering, fine-tuning data poisoning, model stress/DOS, supply chain attacks on model dependencies, and more.
Their methodology blends human-led adversarial probing with custom tooling.
In terms of deliverables, Cybri provides an in-depth report with reproducible exploit prompts, a “jailbreak catalog” of any prompts that got the AI to misbehave, and a clear remediation plan.
This comprehensive, cloud-based model makes Cybri a flexible yet powerful choice for organizations deploying generative AI systems.
Strengths:
- PTaaS model for agility
- AI-specific expertise
- Maps tests to compliance guidelines
- Actionable reporting
2. Secureworks
Best for: Large enterprises seeking a top-tier security firm with global presence and threat intel capabilities to test their AI systems.
Secureworks is a well-known name in cybersecurity, and they have extended their services into AI and machine learning security assessments. While not always branded separately as LLM pentesting, Secureworks brings a structured approach and credibility that many enterprises trust.
Secureworks’ methodology doesn’t explicitly advertise OWASP Top 10, but effectively it covers similar ground due to their comprehensive threat modeling.
Their team’s research arm has studied AI attack surfaces in depth to map out where AI systems can be spoofed, tampered with, made to leak info, or otherwise exploited. Classic issues are checked alongside AI-specific ones, and Secureworks can either simulate an external attacker with no inside knowledge of your model, or use details you provide to dig even deeper.
Deliverables from Secureworks include a professional report with technical findings and remediation guidance prioritized by risk, however their enterprise scale may not offer the same niche specialization as some boutique AI security firms.
Strengths:
- Threat intelligence integration
- Holistic threat modeling
- Global and scalable
- Post-pentest support
3. IARM
IARM is a cybersecurity firm that has developed a clear offering around LLM penetration testing. They emphasize standards and frameworks in their testing methodology.
In practice, this means IARM will test for all the big issues, from prompt injection weaknesses, to plugin vulnerabilities, and more. This results in a report that maps all findings to OWASP classifications and compliance ramifications.
On the technical side, IARM can do both black-box and white-box LLM testing. They can engage with your development team to threat-model the AI and then attack those specifically. They also consider misuse and safety testing as part of the scope, essentially blending some red-team style scenarios.
The final deliverables will consist of a comprehensive report, proof-of-concept scripts, and remediation recommendations.
For organizations that prefer a structured, standards-based approach to LLM testing, IARM is a good fit.
Strengths
- Standards-driven testing
- Customized test cases
- Compliance alignment
- Global delivery capability
4. ioSENTRIX
ioSENTRIX has positioned itself as an interesting candidate for testing AI and machine learning systems. They bring a “full stack” mentality to LLM penetration testing, examining every component of your AI deployment, from data ingestion and preprocessing, to the model training process and more.
They can in some cases offer to align the tests with NIST AI RMF and GDPR for compliance, depending on the complexity and scope of the individual test.
ioSENTRIX approach is both manual and automated. They’ve developed proprietary tools/scripts to automate certain checks, but they also have security engineers creatively probing the AI.
After testing, ioSENTRIX provides a detailed report with proof-of-concepts and mitigation strategies.
While their detailed, full-stack approach may extend timelines and budgets compared to more targeted assessments, ioSENTRIX can be a strong choice for those who want a very comprehensive and tailored AI security review.
Strengths:
- AI security specialists
- End-to-end coverage
- Threat modeling & context-driven
- Strong reporting and retest culture
5. COE Security
COE Security is a newer player that explicitly advertises AI and LLM penetration testing services. Their offering centers on identifying vulnerabilities in AI models and the applications around them before those weaknesses can be maliciously exploited [2].
The company offers to examine privacy angles, understanding how user data is handled in the AI pipeline and whether queries to the LLM could leak info, allowing for some level of compliance.
Beyond just testing prompts at the model, they might also review your AI implementation for best practices, to find out if your prompt filtering is adequate, or whether API calls to the model are properly authenticated and rate-limited.
Deliverables from COE typically include a report of vulnerabilities with severity ratings and recommendations.
COE Security has a practical, attacker-led assessment of your AI that can highlight both technical and logical vulnerabilities.
Strengths:
- Comprehensive adversarial testing
- Privacy and safety emphasis
- Direct expert involvement
- Integration with broader security posture
6. White Knight Labs
White Knight Labs has developed an advanced LLM vulnerability scanner in-house, which evaluates your model against exploits and frameworks.
Their key competency lies in checking for issues such as prompt injection susceptibilities, data poisoning backdoors, or open endpoints that allow unauthorized access [3].
While they do align tests with industry best practices, the company’s focus on AI safety testing means you will typically need to order mapping to standards such as OWASP LLM Top 10, ISO/IEC 42001 separately.
Deliverables from White Knight typically include a comprehensive threat report with both automated scan results and manual findings.
White Knight Labs is an ideal partner for organizations deploying high-risk or highly custom AI systems that require a deep, adversarial security assessment, but their advanced, research-oriented approach may come at a premium price point that is not suited for every budget.
Strengths:
- In-house LLM scanner
- Research and innovation
- Specialized attack repertoire
- Framework-informed
7. Silent Grid Security
Silent Grid Security offers an AI/LLM Penetration Testing service that evaluates the resilience of your AI models against attack [4]. Their focus is on uncovering vulnerabilities that could allow an attacker to tamper with model behavior or violate data privacy.
The company offers to map their testing to the most common frameworks for compliance, such as OWASP and NIST.
Silent Grid’s methodology includes adversarial testing and security control review.
Deliverables from their LLM testing include a report of vulnerabilities and their potential impacts. They provide recommendations to mitigate each issue, which could range from fine-tuning the model or adjusting prompts, to adding monitoring for AI misuse.
For a company that wants an attacker’s perspective on their AI in a very practical, risk-focused way, Silent Grid is a solid option.
Strengths:
- Adversarial perspective
- Model manipulation and privacy focus
- Clear risk communication
- Tailored mitigation guidance
How to Scope an LLM Pentest (So Vendors Can Price It)
Deployment surface
Desired depth of testing
Success criteria
Required standards & compliance inputs
Evaluation Criteria & Weighted Scorecard (Copy-Paste Framework)
Methodology & Coverage
Consider what and how the vendor actually tests. A top vendor should explicitly map their tests to the known risk areas for LLMs. This could mean covering all items in the OWASP Top 10 for LLMs. Each of those represents a class of attacks a modern AI system could face. The best providers will be able to test according to these areas.
Next, assess their red-teaming approach. Are they doing human-led adversarial testing or relying mostly on automated scanners? Human-led testing tends to find more subtle, context-specific issues [6], while automated tools can brute-force lots of prompts, a combination is ideal.
Also, check if they offer testing both pre-deployment and in production. Pre-deployment is safer and can be more thorough, whereas production testing ensures your live defenses are checked, good vendors can do either as needed.
Reporting & Remediation
High-quality reporting is crucial, as you want detailed artifacts and actionable guidance. Good reports will document each finding clearly so your team can replicate and understand it. It is also worth considering if they identify guardrail gaps and give you insight into why the AI’s protections failed. Look for evidence of a prioritized fix list, where vulnerabilities are ranked by severity, so you know what to tackle first.
Also, check if the vendor offers to debrief or work with your devs on remediation, some will include a meeting to walk through findings, or even assist in designing patches. Finally, ask about their process for retests or validation, to find out how quickly they can verify your fixes.
Safety & Compliance
Next, assess how well the vendor can address AI safety issues and compliance requirements. Safety in the context of AI means preventing harmful outputs and ensuring alignment with ethical guidelines. Not every pentesting firm is prepared to test that, since it crosses into AI behavior rather than traditional security.
On the compliance side, look for alignment with frameworks like NIST’s AI Risk Management Framework and its Generative AI Profile. Vendors familiar with NIST AI RMF will approach the engagement systematically and will be aware of the unique generative AI risks NIST highlights [7]. The same goes if you have or plan to pursue ISO/IEC 42001.
Breadth of Attack Surface
Data Handling & Privacy
During an LLM pentest, the vendor may need access to sensitive data or at least will generate potentially sensitive output. You must ensure they handle data securely and respect privacy. So consider if the vendor offers data residency options as well as masking/anonymizations to sanitize any production data or prompts you give them in the report. NDA coverage is standard, so verify they will sign an NDA and maybe specific data protection agreements if needed.
Team & Track Record
Pricing, Timeline & What “Good” Deliverables Look Like
An LLM penetration test usually goes through similar phases as a classic pentest, but with AI nuances. First is discovery & threat modeling, where the vendor spends time understanding your AI system architecture and what “crown jewels” or misuse scenarios to focus on.
Next is the adversarial testing phase, often considered the core part where testers attempt attacks. This phase might be a few days to a few weeks depending on scope. After active testing, there’s the reporting phase, they compile findings, evidence, and recommendations. Finally, a retest window is commonly offered.
Based on this timeline, decide if you need a one-time assessment or ongoing testing. Time-boxed pentests are great for a snapshot, while continuous testing is useful if you do frequent model updates or prompt tweaks. For most, a time-boxed test prior to deployment and then periodic re-tests is a good balance.
What Good Deliverables Looks Like
A high-quality LLM pentest deliverable will contain several important components, such as:
- An executive summary, which is a non-technical summary of overall risk posture, key findings, and recommended actions.
- Detailed findings for each vulnerability or issue discovered, with a clear write-up including description of the issue, exploitation steps, impact, and recommended remediation.
- Exploit chains and proofs of concept, as in more complex cases, can better illustrate an exploit chain and provide supporting evidence such as screenshots or logs.
- Jailbreak & prompt catalog, containing all the notable prompt attempts, especially those that succeeded in breaking rules. This can serve as regression tests for your team, as you’ll want to ensure those exact attacks don’t work after you apply fixes.
- A guardrail gap analysis, which is a report that should discuss why certain failures happened, as this indicates a gap in the instruction set.
- Synthetic test data or scripts can also be provided by some vendors, such as a set of adversarial prompts. allowing you to use them in future testing.
- Prioritized remediation backlogs can be an important deliverable, typically consisting of a table or list of issues sorted by severity, often with an ID, risk rating, finding title, and short fix recommendation.
Retest results are also important if a retest was performed, and in this case the deliverable might include an appendix noting which issues were fixed and retested successfully, and which remain open.
RFP / Vendor Question Bank (Copy-Paste)
When engaging potential LLM pentesting vendors, asking the right questions will illuminate their capabilities. Below is a list of key questions to include in your RFP or vendor questionnaire, along with why they matter and what to look for in answers:
- “Which OWASP LLM Top 10 risks do you cover, and how? Provide sample test cases” This question checks that the vendor is fluent in the well-known LLM risk areas and has concrete methods to test each. High-quality answers will provide specific details and risks like prompt injection, data poisoning or model denial-of-service.
- “How do you align to NIST AI RMF and (if relevant) the NIST GenAI Profile?” This gauges whether the vendor’s process is comprehensive and risk-driven. The NIST AI Risk Management Framework is becoming a baseline for AI governance; a strong vendor will be aware of it and might even structure their engagement to cover its functions.
- “Can you test agents/tools, RAG retrieval, and data-exfil paths end-to-end?” Many LLM applications have components beyond the core model. If a vendor says they only test the model in isolation, that can be a red flag for limited scope. Most companies will prefer a wider test that includes all components.
- “What’s your data-handling policy (logs, prompts, artifacts, retention, residency)?” This is crucial for trust and compliance. This question asks how they protect any sensitive data involved in the test. Good answers should show they have considered the topic already and have procedures in place.
- “What retest window is included?” You want to know if they will verify fixes and how that works. Most reputable vendors include one retest of the significant issues within X days as part of the service. If a vendor doesn’t include a retest, clarify if it’s an extra cost.
“Do you provide developer-ready tickets and policy-hardening guidance (not just findings)?” This question differentiates a vendor who simply hacks from one who truly helps you fix and improve. Developer-ready tickets means they can provide the output in a format that your engineering teams can readily use, perhaps they integrate with Jira or at least provide clear descriptions and steps to reproduce that developers appreciate.
Final Thoughts & Next Steps
Generative AI introduces new security challenges that require specialized LLM penetration testing. The key is to choose a vendor with deep expertise in both AI and security, a thorough methodology, and a focus on actionable results.
We’ve reviewed some top firms, from Cybri’s on-demand platform to White Knight Labs’ research-driven approach. Your choice depends on your needs, as large enterprises may prefer the scale of a firm like Secureworks, while others might benefit from the focused expertise of a boutique.
Your next step could be to contact a few vendors. Have a scoping call to discuss your use case and evaluate their proposals based on scope and value, not just price. Remember, LLM security is a rapidly evolving field, so consider making regular testing part of your long-term strategy.
As a provider of advanced pentesting services focused on AI, Cybri stands ready as an excellent choice to support you in this journey, combining seasoned security testers with AI-specific know-how and a user-friendly delivery platform. Contact us today and learn more.
References
- Owasp Foundation. (n.d.). OWASP Top 10 for Large Language Model Applications
- Cybri. (n.d.). New York Penetration Testing Services
- COE Security. (n.d.). AI & LLM Penetration Testing
- White Knight Labs. (n.d.). LLM Security Testing Services – Safeguard AI Models
- SilentGrid. (n.d.). AI/LLM Penetration Testing – AI Security Assessment
- Airside Labs. (June 2025). Alternatives to NCC Group for LLM Pen Testing
- Arxiv. (October 2024). The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead?
- DLA Piper. (July 2024). NIST releases its Generative Artificial Intelligence Profile: Key points
