April 24, 20265 min read
Why MCP Is the Shadow IT of AI
Every team is wiring agents to MCP servers with the same casual trust they once showed Dropbox installs. The attackers have already noticed.
ai-securitymcpagent-security
Agentic AI Penetration Testing
Breaking AI
with AI.
Full-spectrum agentic AI penetration testing. Autonomous hackbots that probe, exploit, and report on your LLMs, agents, RAG systems, MCP servers, and chatbots before an attacker does. The same hackbots run conventional web and infrastructure pentests. AI-focused, not AI-only.
6+
Hackbot Architectures
13
Attack Intent Categories
25+
Attack Techniques
21+
Evasion Methods
Research aligned withOWASP LLM Top 10MITRE ATLASNIST AI RMFArcanum Taxonomy
The Paradigm Shift
AI vulnerabilities are semantic, not syntactic
Traditional scanners fuzz bytes. AI vulnerabilities live in meaning. Prompt injection is not a malformed HTTP request. It is a sentence that sounds helpful but changes the model's behavior. The only thing that can probe meaning at scale is another AI.
01 ···
Non-deterministic targets
Same prompt, different response every time. Static test suites are useless. AI pentesting must be adaptive, running hundreds of variations and measuring success probabilistically.
02 ···
Attacks chain across turns
The most dangerous AI attacks are multi-turn conversations that gradually shift context, build trust, and bypass guardrails that catch single-prompt attempts. Only AI maintains that strategic state.
03 ···
Tool access changes everything
A jailbroken chatbot says something inappropriate. A jailbroken agent with database access exfiltrates data, modifies records, and executes code. The blast radius is orders of magnitude larger.
What We Test
Full-spectrum AI attack surface
Every layer of your AI stack tested by autonomous hackbots. LLMs, agents, RAG pipelines, chatbots, and the integrations between them.
LLM Penetration Testing
Adversarial testing of large language models. Prompt injection, jailbreaking, system prompt extraction, guardrail bypass, and alignment manipulation at scale.
AI Agent Security
Testing autonomous agents with tool access, code execution, and API keys. A jailbroken agent with database access is not embarrassing. It is catastrophic.
RAG & Pipeline Attacks
Poisoning vector databases, manipulating retrieval context, corrupting training data upstream. Testing the full stack, not just the model sitting on top.
Chatbot & App Testing
End-to-end security testing of customer-facing AI. Business logic bypass, data exfiltration, policy override, and multi-turn conversation attacks.
Our Process
Recon. Build. Test. Report.
Every engagement starts with mapping the attack surface and ends with actionable findings. The hackbots do the work between.
01 ···
Reconnaissance
Map the AI attack surface. Identify model architectures, tool integrations, RAG pipelines, and data flows. Understand what the system can do before testing what it should not.
02 ···
Build
Develop autonomous hackbots tailored to the target. Adaptive attack chains, multi-turn exploitation strategies, and evasion techniques built for this specific system.
03 ···
Test
Deploy hackbots against the target in controlled environments. Thousands of adversarial probes, probabilistic success measurement, and automatic attack escalation.
04 ···
Report & Harden
Full findings report with reproduction steps, risk ratings, and remediation guidance. Open research published to strengthen the entire ecosystem.
Research & Insights
View all researchLatest from the lab
Mar 1810 min read
Your First AI Red Team Skill: From Zero to Weaponized in 30 Minutes
Claude Code is the best AI tool I've found for offensive security, but these concepts work with Gemini and other powerful AI agents too. Here's how to build your first AI red team skill and why it changes everything about how you operate.
Mar 1914 min read
Reconnaissance at Machine Speed: AI-Powered Target Mapping
A manual recon of an AI application takes 2-4 hours. With Claude Code and kali-mcp, it takes 12 minutes. Here's the exact workflow with working skills you can copy.
Every AI system deployed untested
is a system waiting to be exploited
Your LLMs, agents, and chatbots face attack categories that traditional security tools cannot test. AI vulnerabilities are semantic. Only AI can find them at scale.