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AI PENETRATION TESTING SERVICES
Specialized AI Penetration Testing Service for LLMs, AI Agents & Generative AI Systems
Detect. Analyze. Secure. Before Attackers Find the Gap First.
EXPLOIT BEFORE ATTACKERSREAL-WORLD ATTACK SIMULATIONAI-POWERED ANALYSISDEEP VULNERABILITY INSIGHTS
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Real-World Attack SimulationSimulate advanced threats across your entire environment.
AI-Driven Threat DetectionLeverage AI to detect and predict attack paths.
Certified Security ExpertsWork with industry-leading ethical hackers.
Detailed Reports & RemediationClear findings with actionable steps to fix vulnerabilities.
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Your LLM is answering customer questions. Your copilot is touching internal systems. Your agent is calling APIs and making decisions with no humans in the loop. None of that was tested the way it needs to be. Traditional penetration testing was built for a world of static code and known attack signatures. AI systems don’t fail that way, they fail through manipulated prompts, poisoned data, and models that do exactly what they’re told by exactly the wrong person. This isn’t a checklist audit. It’s an actual attempt to jailbreak, poison, and exfiltrate through your AI stack.
Why Choose Our
AI Penetration Testing?
AI systems introduce attack surfaces that traditional security assessments were never built to test.
✓AI creates attack surfaces traditional testing was never built for: LLMs, RAG, agents, plugins, and third-party models all introduce risk a standard web app scan won’t touch
✓Prompt injection and jailbreaks are already being attempted against production systems, often without triggering a single alert
✓Sensitive data leaks through the model itself: PII, credentials, system prompts, and training data can surface in outputs no one thought to test
✓Agentic AI multiplies the blast radius: an agent with excessive permissions doesn’t just leak data, it can act on it
✓Regulators are catching up faster than most security teams: ISO/IEC 42001, NIST AI RMF, and the EU AI Act are no longer optional reading
Common triggers: AI product launch · cloud or AI infrastructure migration · compliance audit · cyber insurance requirement · major model change · M&A · security incident · agent deployment.
AI Penetration Testing Process
01Scope & ObjectivesSystems, goals & rules of engagement.
02Map Attack SurfaceLLMs, agents, RAG, APIs & data.
03AI Threat ModelingTrust boundaries & abuse scenarios.
04Prompt & Input TestingInjection, jailbreaks & guardrails.
05Data & RAG TestingLeakage, poisoning & vector stores.
06Model & LLM TestingManipulation, extraction & inversion.
07Agentic & Tool TestingAgency, permissions & tool calls.
08API & InfrastructureAuth, cloud, containers & network.
09AI Red TeamingChained adversary simulations.
10Risk & Impact AnalysisExploitability & business impact.
11Report & RemediateEvidence, fixes & developer guidance.
12Retest & ValidateVerify fixes and issue completion letter.
A standard engagement runs 1–3 weeks depending on scope — and retesting is included, not sold as an add-on.
Comprehensive Penetration Testing Services
We test your entire digital environment using advanced methodologies and AI-driven tools.
Web Application
Network Infrastructure
Testing
Find vulnerabilities in internal and external networks.
Mobile Application
Testing
Secure your iOS and Android applications.
Cloud Security
Testing
Assess AWS, Azure, Google Cloud and hybrid environments.
API Security
Testing
Detect and exploit API vulnerabilities.
Wireless Security
Testing
Identify risks in Wi-Fi networks and access points.
Social Engineering
Testing
Test human defenses against phishing and manipulation.
IoT & Embedded
Testing
Secure connected devices and embedded systems.
AI Penetration Testing Service for LLM Applications Engagement Delivers
If your product runs on an LLM — OpenAI, Anthropic, Mistral, Llama, or a self-hosted fine-tuned model — your attack surface looks nothing like a traditional web application, and most vendors haven't built an AI penetration testing service for LLM applications that accounts for the difference.
Every AI penetration testing service for LLM applications engagement targets the full trust boundary of your stack: system prompt exposure, instruction hierarchy bypass, tool call exploitation, RAG injection, retrieval poisoning, and output manipulation that slips past your safety guardrails without tripping a single alert.
What every engagement delivers:
- Proof-of-concept exploits for every critical and high-severity finding — demonstrated, not theoretical
- CVSS-scored findings with business impact mapped to your specific product and data flows
- Developer-facing remediation playbook with framework-specific, code-level guidance
- Retest included within 60 days of remediation, at no extra cost
- Attestation letter formatted for compliance, legal review, and enterprise procurement
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500+Clients Protected
100M+Threats Analyzed Monthly
99.9%Vulnerability Detection Rate
24/7Security Expertise
Benefits of AI Penetration Testing
✓Identifies AI-specific vulnerabilities — prompt injection, jailbreaks, RAG poisoning, and agentic risks a standard scan won’t surface
✓Protects sensitive data and IP — prevents leakage of confidential data, system prompts, credentials, and training data
✓Reduces business risk — finds exploitable weaknesses before attackers turn them into financial or reputational damage
✓Validates AI governance — confirms your policies, controls, and guardrails actually work under real attack conditions
✓Supports compliance and audit readiness — evidence formatted for ISO/IEC 42001, NIST AI RMF, and internal AI policy requirements
✓Builds trust and transparency — proof for customers and partners that your AI has been independently, adversarially tested
✓Prioritizes security investment — remediation resources go where the actual business risk is, not where it’s easiest to fix
✓Enables continuous AI assurance — retesting as models, prompts, and integrations change, instead of a one-time check-the-box exercise
OUR SERVICES AREA
AI Penetration Testing Services
Explore All Services →When Do You Need an AI Pentest?
Nine common triggers for testing AI systems.
01
AI Product Launch
Establish a testing baseline before release.
02
Cloud or AI Migration
Catch new configuration and integration risks.
03
Audit or Customer Review
Provide evidence of security testing.
04
Cyber Insurance Review
Address AI security questions from insurers.
05
Major Model Change
Reassess risk after model or infrastructure changes.
06
M&A or Partnership
Assess inherited AI systems and dependencies.
07
Security Incident
Investigate exposure and validate fixes.
08
AI Copilot or Agent Rollout
Test autonomy, permissions and tool access.
09
Annual AI Assessment
Keep pace with changing models, prompts and threats.
Test at launch, after major changes and on a regular cadence.
Types of AI Penetration Testing & AI Red Teaming We Perform
01
LLM Application Testing
Prompt injection, jailbreaks & system prompts.
02
AI Red Teaming
Adversarial scenarios across the AI product.
03
RAG & Data Poisoning
Tampered documents, sources & embeddings.
04
AI Agent Testing
Excessive agency & unauthorized tool actions.
05
Model Security
Extraction, inversion & membership inference.
06
Adversarial ML
Manipulated inputs & unsafe decisions.
07
AI API & Integration
Auth, plugin abuse & business logic.
08
AI Data Leakage
PII, PHI, credentials & confidential data.
09
AI Access Testing
RBAC, privilege escalation & identity.
10
AI Code Generation
Insecure code, secrets & dependencies.
11
AI Cloud & Infrastructure
Endpoints, containers, AWS, Azure & GCP.
12
Multimodal AI Testing
Text, image, audio & video payloads.
Every finding is manually validated and exploit-confirmed before it reaches your report — never a scanner output relabeled as a pentest finding.
Our AI Penetration Testing Strategy & Methodology
Strategy 1: Pentesting the AI System
Structured around the OWASP GenAI and Agentic AI Top 10, across five core vectors:
- Input Manipulation (Prompt Injection & Jailbreaking) — crafting adversarial, multi-turn inputs to bypass safety guardrails
- Excessive Agency & Tool Misuse — mapping every API, tool, and plugin your AI can reach, then hijacking its intent
- Indirect Prompt Injection via RAG Poisoning — feeding compromised documents into your retrieval system
- Sensitive Information Disclosure — targeting telemetry, embeddings, and hidden data channels
- Insecure Output Handling — treating model output as untrusted input, then testing for XSS, SQL injection, and SSRF downstream
Strategy 2: AI-Driven Pentesting — Using AI as the Attacker
Target definition, autonomous multi-agent recon, automated proof-of-exploit generation, and human validation of every automated finding. Every autonomous tool runs inside a strict human-in-the-loop containment model — hardcoded safety parameters prevent any automated tool from causing unintended impact on your production environment.
AI Penetration Testing Frameworks & Standards
OWASP Top 10 for LLM Applications · OWASP GenAI Security Project · OWASP Agentic AI Security Guidance · MITRE ATLAS · MITRE ATT&CK · NIST AI RMF · NIST SP 800-115 · PTES · ISO/IEC 42001 · ISO/IEC 23894.
They shape how every engagement is scoped, executed, and reported — from the first threat model to the final attestation letter.
01
OWASP Top 10 for LLM Applications
Prompt injection, disclosure & output risks.
02
OWASP GenAI Security Project
Generative AI security guidance.
03
OWASP Agentic AI Guidance
Tool use, autonomy & identity risks.
04
MITRE ATLAS
Adversarial AI tactics and techniques.
05
MITRE ATT&CK
Enterprise attack behavior & infrastructure.
06
NIST AI RMF
Govern · Map · Measure · Manage.
07
NIST SP 800-115
Technical security testing methodology.
08
PTES
End-to-end penetration test execution.
09
ISO/IEC 42001
AI management systems & governance.
10
ISO/IEC 23894
AI risk identification & treatment.
Who Needs AI Penetration Testing Services?
If your organization has shipped anything with a model behind it, this applies to you. The pattern holds across every sector we work with:
LLMs, Chatbots, Copilots & RAG
Generative AI touching real users or real data
Autonomous & Agentic AI
Access to tools, APIs or databases; actions without approval at every step
Regulated Organizations
Finance, healthcare & government; PCI DSS, HIPAA or FedRAMP exposure
SaaS & Technology Companies
AI embedded in products, platforms or customer-facing APIs
Enterprise AI Copilots
Used across engineering, support or internal operations
AI Governance Audit Readiness
Preparing for ISO/IEC 42001 or NIST AI RMF assessments
If AI accesses sensitive data, makes decisions, generates critical output, talks to customers or takes action through tools, include it in your security program.
Here’s the simple rule we test against: if your AI can access sensitive data, make a decision, generate business-critical output, talk to a customer, or take an action through a tool or API, it belongs in your security program, not outside it.
OUR CYBERSECURITY SOLUTIONS
How Our Engagement Works
From initial scope to validated findings and remediation, follow a clear, evidence-led process.
OUR DELIVERY PROCESS · STEP 01
Scope & Objectives
Agree on assets, priorities, rules of engagement and the business outcomes that matter.
- Asset inventory
- Risk priorities
- Testing boundaries
OUR DELIVERY PROCESS · STEP 02
Assessment & Validation
Examine controls and attack paths, then validate relevant findings with evidence.
- Manual verification
- Threat scenarios
- Evidence capture
OUR DELIVERY PROCESS · STEP 03
Reporting & Remediation
Translate technical findings into business impact, ownership and actionable fixes.
- Executive summary
- Technical report
- Remediation roadmap
OUR DELIVERY PROCESS · STEP 04
Retesting & Assurance
Reassess agreed fixes and provide evidence of closure and residual risk.
- Fix verification
- Residual risk
- Follow-up guidance
Frequently Asked Questions
What is penetration testing?
Penetration testing is a simulated cyber attack against your systems, networks, applications or cloud environment to identify security vulnerabilities before real attackers can exploit them.
How does AI improve penetration testing?
AI-assisted analysis helps correlate findings, identify attack paths and prioritize risks while expert testers validate meaningful weaknesses.
How often should we conduct penetration testing?
Frequency depends on your risk, compliance needs and rate of change. Many organizations test annually and after major changes.
Do you provide remediation support?
Yes. Findings include prioritized remediation guidance, with retesting available to validate fixes.
Which compliance standards do you cover?
Testing can support objectives associated with ISO 27001, SOC 2, HIPAA, PCI DSS and other security requirements.