Laboratory for Adversarial Probing & Simulation
AI-native offensive security · early stage

Offensive security, engineered for the age of AI.

We're building an AI-native solution for autonomous adversarial simulation, vulnerability research, and reproducible security testing, where agents do the probing and humans validate what they find.

Explore our solution
Why now

Attack surfaces are moving faster than security teams can test them.

Software ships continuously, systems now embed LLMs and autonomous agents, and point-in-time pentests can't keep up. Offensive security needs to become continuous, scalable and evidence-driven.

01 / scale

Manual testing doesn't scale

Expert red teamers are scarce. Most assets are tested once a year, if at all, and coverage gaps are where real attacks happen.

02 / new surface

AI systems are a new attack surface

Prompt injection, tool abuse, data exfiltration through agents and MCP integrations: AI applications need adversarial evaluation of their own.

03 / capability

Agents can now reason about exploits

Frontier models can plan, use tools and iterate. Orchestrated carefully and kept under human control, they can multiply what a researcher can cover.

Capabilities

Built for autonomous adversarial work.

Our solution combines LLM orchestration, purpose-built security tooling and rigorous evaluation in one environment, so findings come back reproducible and evidence-backed instead of as hallucinated guesses.

Autonomous adversarial simulation

Multi-agent campaigns that do recon, chain weaknesses and emulate realistic attacker behaviour against scoped targets. Agentic red teaming that runs continuously, not once a year.

attack simulationred teamingagentic AI

Vulnerability research & discovery

Autonomous security agents that review code, fuzz interfaces and form hypotheses, then try to prove or disprove them with working proof-of-concepts in sandboxed environments.

vulnerability discoveryPoC generationsandboxed exec

AI security evaluation

Adversarial testing for LLM applications and agentic systems: prompt injection, jailbreak resilience, tool and MCP-server abuse, data leakage and excessive agency.

LLM red teamingMCP securityevals

Reproducible security testing

Every agent action is logged, versioned and replayable. Findings ship with traces, evidence and deterministic reproduction steps that engineers can verify and regress against.

audit trailreplayCI/CD-ready
Approach

Agents explore. Humans decide.

Our orchestration layer coordinates specialised agents over a tool layer exposed through the Model Context Protocol (MCP). Every high-impact step passes a human-in-the-loop checkpoint.

Scope

Define authorised targets, rules of engagement and guardrails, all machine-enforced.

policy.yaml

Plan

A planner agent maps the attack surface and breaks the objective into hypotheses.

LLM orchestration

Probe

Specialist agents run tools via MCP servers in isolated sandboxes and iterate on results.

MCP · sandbox

Validate

Human-in-the-loop review confirms exploitability and approves any escalation.

HITL checkpoint

Report

Evidence-backed findings with replayable traces, severity and remediation guidance.

reproducible
Principles

Offensive capability, built responsibly.

Autonomous offensive tooling is powerful. We treat safety, authorisation and accountability as core product features, not afterthoughts.

  • [01]
    Authorised targets onlyScope is enforced at the orchestration layer. Agents cannot act outside it.
  • [02]
    Human-in-the-loop by defaultExploitation and any impactful action require explicit human approval.
  • [03]
    Evidence over speculationNo finding without proof. Every claim is backed by a reproducible trace.
  • [04]
    Full auditabilityEvery prompt, tool call and decision is logged for review and compliance.
  • [05]
    Defenders firstWe build for security teams, researchers and AI builders hardening their own systems.
Roadmap

Early stage, building in the open.

Our lab is an early-stage startup. Here's where we are and where we're heading.

Now

Research & core engine

  • Agent orchestration runtime
  • MCP-based security tool layer
  • Sandboxed execution & tracing
  • Internal benchmarks on intentionally vulnerable targets
Next

Design partners

  • Private pilots with security teams
  • AI application red-teaming evals
  • Human-in-the-loop review console
  • Reproducible reporting & export
Later

Continuous adversarial testing

  • CI/CD & ticketing integrations
  • Continuous attack simulation
  • Open evaluation benchmarks
  • Self-hosted deployments