AI & LLM Penetration Testing. Secure your generative AI Systems.
As organizations rush to deploy ChatGPT, Copilot, and custom LLMs, attackers are finding new ways to exploit them. We test your AI systems for prompt injection, jailbreaks, data leakage, and the OWASP Top 10 for LLM Applications.
Why AI security cannot wait
Generative AI adoption is outpacing security. Organizations deploying LLMs face novel attack vectors that traditional security tools cannot detect.
AI security risks organizations face
Generative AI introduces attack vectors that don't exist in traditional applications. Your security team may not have the expertise to identify and mitigate these novel risks.
Prompt injection attacks
Attackers craft malicious prompts that override system instructions, bypass safety guardrails, or manipulate LLM behavior. Direct and indirect injection can lead to unauthorized actions.
Sensitive data leakage
LLMs can inadvertently expose training data, PII, API keys, or confidential business information. Users may unknowingly extract sensitive data through clever prompting.
Insecure output handling
LLM outputs trusted without validation can lead to XSS, SQL injection, command injection, or server-side request forgery when used in downstream systems.
RAG poisoning
Retrieval-Augmented Generation systems can be manipulated by injecting malicious content into knowledge bases, causing LLMs to return compromised or harmful information.
Supply chain vulnerabilities
Pre-trained models, fine-tuning datasets, and third-party plugins may contain backdoors, biases, or vulnerabilities that compromise your AI applications.
Insecure plugin design
LLM plugins with excessive permissions, inadequate input validation, or weak access controls create pathways for privilege escalation and unauthorized access.
Excessive agency & autonomy
AI agents with broad permissions can take unintended actions: sending emails, executing code, or accessing systems beyond their intended scope.
Model theft & extraction
Attackers can reverse-engineer proprietary models through systematic querying, stealing your competitive advantage and intellectual property.
AI compliance gaps
EU AI Act, NIST AI RMF, and emerging regulations require AI risk management. Most organizations lack frameworks to demonstrate AI security compliance.
Benefits of AI & LLM penetration testing
Proactive AI security testing protects your organization from novel attack vectors while enabling safe AI adoption.
Identify AI-specific vulnerabilities
Uncover prompt injection, jailbreaks, and data leakage risks before attackers exploit them. Test against OWASP LLM Top 10 and MITRE ATLAS.
Detailed exploitation scenarios with reproduction steps and fix recommendations
Risk quantification for board reporting and insurance requirements
Protect sensitive data
Prevent LLMs from leaking training data, PII, or confidential information. Validate data handling and privacy controls.
Data extraction testing, membership inference attacks, prompt leaking detection
GDPR compliance evidence, reduced breach liability, customer trust
Validate safety guardrails
Test content moderation, output filtering, and safety mechanisms. Ensure your AI cannot be manipulated to produce harmful content.
Jailbreak testing, guardrail bypass attempts, adversarial prompt crafting
Brand protection, legal risk mitigation, ethical AI assurance
Meet regulatory requirements
Demonstrate AI risk management for EU AI Act, NIST AI RMF, and sector-specific requirements. Generate audit-ready evidence.
Security testing documentation mapped to compliance frameworks
Regulatory compliance, audit readiness, reduced legal exposure
Enable safe AI innovation
Deploy AI applications with confidence. Security validation enables faster adoption by reducing risk concerns from legal and security teams.
Clear security requirements, pre-deployment testing, remediation guidance
Accelerated AI adoption, competitive advantage, stakeholder confidence
Prevent AI-powered attacks
Understand how attackers use AI against you. Test for AI-enhanced phishing, deepfakes, and automated attack generation.
Adversarial AI testing, attack simulation, defense validation
Threat awareness, proactive defense posture, board-level reporting
Comprehensive AI & LLM testing categories
We test AI systems against the OWASP Top 10 for LLM Applications, MITRE ATLAS, and emerging AI threat frameworks.
LLM01: Prompt Injection
Systematic testing for direct and indirect prompt injection vulnerabilities. We craft adversarial prompts that attempt to override system instructions, extract hidden prompts, or manipulate LLM behavior.
Learn MoreHow we conduct AI & LLM testing
Our AI security testing methodology combines automated tooling with expert manual testing, aligned with OWASP LLM Top 10 and MITRE ATLAS frameworks.
Scoping & Threat Modeling
Understand your AI architecture, use cases, and threat landscape. Define testing scope, access requirements, and success criteria for the engagement.
Automated scanning & fuzzing
Deploy automated AI security testing tools to identify common vulnerabilities and generate baseline coverage across prompt injection, output handling, and data leakage vectors.
Manual exploitation
Expert testers manually attempt to exploit vulnerabilities, chain attacks, and bypass defenses. We go beyond automated tools to find complex, logic-based vulnerabilities.
RAG & Integration Testing
Test retrieval-augmented generation systems, plugins, and integrations with external systems. Identify risks at the boundaries of your AI system.
Compliance & Risk Assessment
Map findings to regulatory requirements and risk frameworks. Assess compliance posture against EU AI Act, NIST AI RMF, and relevant standards.
Reporting & Remediation
Comprehensive reporting with executive summary, technical findings, and prioritized remediation guidance. We support your team through the fix process.
AI & LLM security testing deliverables
Actionable deliverables that enable your team to understand, prioritize, and remediate AI security risks.
Executive summary
Board-ready overview of AI security posture with risk ratings, business impact, and strategic recommendations.
- Risk posture score
- Business impact analysis
- Regulatory implications
- Strategic recommendations
Technical findings report
Detailed vulnerability documentation with OWASP LLM Top 10 mapping, exploitation scenarios, and proof of concept.
- Vulnerability details
- Attack scenarios
- Screenshots/recordings
- CVSS-like scoring
- OWASP mapping
Remediation playbook
Prioritized fix recommendations with implementation guidance, code examples, and defense patterns.
- Fix priority matrix
- Implementation guidance
- Guardrail recommendations
- Defense patterns
Attack tree analysis
Visual representation of attack paths showing how vulnerabilities can be chained for maximum impact.
- Attack chain diagrams
- Exploitation paths
- Impact scenarios
- Risk combinations
Compliance gap analysis
Assessment against EU AI Act, NIST AI RMF, and relevant frameworks with specific gaps and remediation steps.
- Framework mapping
- Compliance gaps
- Evidence requirements
- Control recommendations
Retest & attestation
Verification testing of remediations with formal attestation letter for stakeholders and auditors.
- Remediation verification
- Attestation letter
- Delta report
- Compliance evidence
Frequently asked questions
Answers to common questions about AI and LLM security testing.
The OWASP Top 10 for LLM Applications is a framework identifying the most critical security risks in LLM-based systems. It includes: LLM01 (Prompt Injection), LLM02 (Insecure Output Handling), LLM03 (Training Data Poisoning), LLM04 (Model Denial of Service), LLM05 (Supply Chain Vulnerabilities), LLM06 (Sensitive Information Disclosure), LLM07 (Insecure Plugin Design), LLM08 (Excessive Agency), LLM09 (Overreliance), and LLM10 (Model Theft). We test against all of these vectors.
We test all types of generative AI and LLM applications: ChatGPT/GPT-4 integrations, custom fine-tuned models, RAG-based systems, AI agents with tool use, Copilot implementations, customer service chatbots, internal knowledge bases, and AI-powered applications. We also test supporting infrastructure including vector databases, embedding pipelines, and API gateways.
AI systems have unique attack surfaces that don't exist in traditional applications. Prompt injection isn't SQL injection. It requires understanding how LLMs process context. Data leakage risks are probabilistic, not deterministic. We use specialized tools and techniques for AI systems while also testing traditional security aspects like API security, authentication, and infrastructure.
Yes. We test custom models whether fine-tuned on your data, trained from scratch, or based on open-source foundations. We assess model-specific risks including training data extraction, backdoors introduced during fine-tuning, and unique vulnerability patterns in your model's behavior.
Most enterprise AI applications use third-party LLM APIs. We test your application layer: system prompts, RAG pipelines, plugins, output handling, and integrations. The risks are in how you use the API, not the API itself. We identify vulnerabilities in your implementation that could be exploited regardless of the underlying model.
Yes, we can include bias and fairness testing as part of a comprehensive AI assessment. This is especially important for AI systems making decisions affecting people (hiring, lending, etc.) and for EU AI Act compliance. We test for discriminatory outputs and document fairness concerns for your risk management.
We work with you to define safe testing approaches. This may include testing against staging environments, rate-limited production access, or isolated test instances. For critical systems, we start with lower-risk tests and escalate carefully. We coordinate closely with your team to avoid disruption.
We map findings to OWASP LLM Top 10, MITRE ATLAS, EU AI Act requirements, NIST AI RMF, and ISO 42001. We can also map to sector-specific requirements like FDA guidance for AI in medical devices or financial services AI regulations. This enables audit-ready documentation.
A typical AI security assessment takes 2-4 weeks depending on scope. Simple chatbot assessments may take 2 weeks, while complex multi-model systems with RAG, plugins, and agents may require 4+ weeks. We provide a detailed timeline after understanding your specific environment.
Yes. AI systems evolve rapidly: prompts change, models get updated, and new attack techniques emerge. We offer continuous AI security monitoring and periodic re-assessments to ensure your defenses keep pace with the threat landscape. This includes alerting on new vulnerabilities affecting your AI stack.
AI security specialists
Our team combines deep AI/ML expertise with offensive security skills to identify vulnerabilities others miss
Don't let your AI become your biggest liability.
Generative AI is transforming business, but insecure AI can expose sensitive data, bypass controls, and create regulatory nightmares. Get an AI security assessment before attackers find your vulnerabilities first.