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.
Cisco SD-WAN Manager zero-day CVE-2026-20245 exploited with no patch, Microsoft ships record 206-flaw Patch Tuesday with 3 zero-days, ShinyHunters claims 275M Instructure records, and OWASP confirms prompt injection in 73% of production AI deployments.
Read the latest CTI briefClaude Code 2.1.175 lands enforceAvailableModels governance, the MCP 2026-07-28 spec release candidate drops a stateless core, and Anthropic ships sandboxed Managed Agents plus a DXC regulated-industry deal.
Read the latest AI brief115 intelligence briefs. Researched, written, and published every morning by an autonomous agent pipeline. The same engineering we test with.
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.
Same prompt, different response every time. Static test suites are useless. AI pentesting must be adaptive, running hundreds of variations and measuring success probabilistically.
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.
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.
Every layer of your AI stack tested by autonomous hackbots. LLMs, agents, RAG pipelines, chatbots, MCP servers, and the integrations between them.
Adversarial testing of large language models. Prompt injection, jailbreaking, system prompt extraction, guardrail bypass, and alignment manipulation at scale.
Testing autonomous agents with tool access, code execution, and API keys. A jailbroken agent with database access is not embarrassing. It is catastrophic.
Poisoning vector databases, manipulating retrieval context, corrupting training data upstream. Testing the full stack, not just the model sitting on top.
End-to-end security testing of customer-facing AI. Business logic bypass, data exfiltration, policy override, and multi-turn conversation attacks.
Probing the Model Context Protocol surface. Tool definition injection, authorization bypass between tool calls, context pollution through tool output, and capability creep across an agent's connected tools.
Every engagement starts with mapping the attack surface and ends with actionable findings. The hackbots do the work between.
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.
Develop autonomous hackbots tailored to the target. Adaptive attack chains, multi-turn exploitation strategies, and evasion techniques built for this specific system.
Deploy hackbots against the target in controlled environments. Thousands of adversarial probes, probabilistic success measurement, and automatic attack escalation.
Full findings report with reproduction steps, risk ratings, and remediation guidance. Open research published to strengthen the entire ecosystem.
The word agent gets used for a chatbot with a system prompt, a script in a loop, and a system that plans and acts on its own. Three different animals. Only one can hurt you. A red teamer's guide to what an AI agent actually is and what agentic really means.
Every team is wiring agents to MCP servers with the same casual trust they once showed Dropbox installs. The attackers have already noticed.
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.
Every engagement is scoped to your stack, your threat model, and your tolerance for risk. Start with the shape that fits, and we tailor from there.
A full-spectrum assessment of an LLM application or agent. Autonomous hackbots run thousands of adversarial probes against prompt handling, guardrails, tool access, and conversation state, then chain the weaknesses they find into realistic kill chains a single-prompt scanner would miss.
Scoping covers model endpoints, system prompts, connected tools, and the data the system can reach.
A focused review of the Model Context Protocol and tool layer your agents depend on. We probe tool definition injection, authorization gaps between tool calls, context pollution through tool output, and capability creep across every connected server an attacker could pivot through.
Scoping covers MCP servers, tool schemas, authorization boundaries, and inter-tool trust assumptions.
A sustained adversary simulation against your AI stack and the humans and systems around it. We model a determined attacker over time, combining semantic attacks, multi-turn social engineering, and infrastructure pivots to show how AI weaknesses become business impact.
Scoping covers objectives, rules of engagement, target systems, and the threat actors we emulate.
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.