Test Your AI Systems Like an Attacker Would.
We simulate real-world attacks against your LLMs, agents, and AI pipelines. Find the vulnerabilities before someone else does.
Attack Surface Coverage
Traditional + AI. Both Covered.
Most AI systems run on traditional infrastructure. We test the full stack, not just the model layer.
Traditional Security
- DNS & Domain Security
- SSL/TLS Configuration
- HTTP Security Headers
- WAF Detection & Bypass
- Infrastructure & Network
AI Security
- Prompt Injection & Jailbreaks
- System Prompt Extraction
- Data Exfiltration via LLM
- Agent Exploitation & Tool Abuse
- RAG Poisoning & Context Manipulation
Methodology
Structured. Repeatable. Thorough.
Based on OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF. Every finding mapped to recognized frameworks.
Reconnaissance
Map your AI attack surface. We identify every exposed model endpoint, agent workflow, RAG pipeline, and integration point. We also enumerate traditional infrastructure like DNS, SSL, and HTTP configurations.
Threat Modeling
Build an adversary profile for your specific system. We analyze your architecture, data flows, and trust boundaries to determine the most likely and most impactful attack paths.
Active Testing
Execute targeted attacks against your AI systems. Prompt injection variants, jailbreak techniques, context window manipulation, tool-call abuse, and data exfiltration attempts, all mapped to OWASP LLM Top 10 categories.
Exploitation
Chain findings together to demonstrate real business impact. We go beyond isolated vulnerabilities to show how an attacker would combine weaknesses to achieve objectives like data theft, privilege escalation, or system compromise.
Reporting
Deliver a detailed technical report with severity ratings, reproduction steps, proof-of-concept payloads, and prioritized remediation guidance. Executive summary included for leadership and compliance teams.
Sample Findings
What We Find
Anonymized examples from real engagements. Every finding includes severity, reproduction steps, and remediation guidance.
System Prompt Extractable via Role Injection
Attacker can extract the full system prompt including internal tool definitions, API schemas, and business logic. Enables targeted follow-up attacks with full knowledge of system constraints.
RAG Context Poisoning via Document Upload
Malicious documents injected into the knowledge base alter model responses for all users. Attacker-controlled content served as trusted answers without any indication of tampering.
Agent Tool-Call Abuse Leads to SSRF
AI agent can be manipulated into making HTTP requests to internal services via its web-browsing tool. Internal API endpoints and metadata services accessible through crafted prompts.
Output Filter Bypass via Encoding Tricks
Content safety filters bypassed using Base64 encoding and token-splitting techniques. Model produces restricted content when instructions are obfuscated across multiple turns.
FAQ
Common Questions
How is an AI pentest different from a traditional pentest?
Traditional pentests focus on network, application, and infrastructure vulnerabilities. An AI pentest adds a full layer of testing specific to machine learning systems: prompt injection, jailbreaking, data exfiltration through model outputs, agent tool-call abuse, and RAG pipeline manipulation. We cover both layers because most AI systems sit on top of traditional infrastructure that also needs to be secure.
What is the typical timeline for an AI penetration test?
Most engagements run 2 to 4 weeks depending on scope. A single LLM endpoint with limited tool access can be tested in under 2 weeks. Multi-agent systems with RAG pipelines, external integrations, and multiple user roles typically require 3 to 4 weeks. We provide a detailed timeline during scoping.
What frameworks and standards do you follow?
Our methodology is built on OWASP LLM Top 10, MITRE ATLAS (Adversarial Threat Landscape for AI Systems), and NIST AI RMF (AI Risk Management Framework). We map every finding to these frameworks so your compliance and risk teams can track remediation against recognized standards.
What do you need from us to get started?
At minimum: access to the AI system (API keys or a test environment), architecture documentation or a walkthrough call, and a list of user roles and permissions. For agent-based systems, we also need documentation on available tools and their capabilities. We handle everything else.
What do we receive at the end of the engagement?
A detailed technical report covering every finding with severity rating, reproduction steps, proof-of-concept payloads, and specific remediation guidance. You also get an executive summary for leadership, a risk heat map, and a 30-minute walkthrough call to discuss findings and answer questions. We include 2 weeks of follow-up support for remediation questions.
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