Autonomous offensive agents that learn your environment, chain exploits, and reason adversarially at machine speed. Not pattern matching. Not vendor demos. Not chatbots playing pretend.
Obsidian-3.2 is our autonomous offensive agent. It runs reconnaissance, hypothesizes attack paths, executes exploit primitives, and re-plans based on what it learns — all without human-in-the-loop for routine decisions. Operators direct objectives; the agent decides how to reach them.
The result is a kind of pressure that no human team can sustain: 24/7 patient exploration of your attack surface at machine speed, with reasoning depth that grows the longer it runs.
Adversaries are already deploying AI to find your weaknesses. Researchers have demonstrated autonomous exploitation against CVEs within hours of disclosure. Threat actors are running LLM-driven phishing campaigns and ransomware affiliate workflows.
Your defenders need to know what an AI adversary will find before one finds you — and the only credible test is to run an actual AI adversary against your environment, under our control, with reportable findings.
The agent reasons across CVEs, misconfigurations, and access primitives to chain multi-step attacks toward defined objectives.
Prompt injection, jailbreaks, training data extraction, and tool-use abuse against your production LLM stack. Full OWASP LLM Top 10 coverage.
Model evasion, membership inference, model extraction, and data poisoning against your ML pipelines and deployed inference endpoints.
Every decision the agent makes is logged with reasoning, evidence, and remediation guidance. Reproducible, auditable, and admissible.
Agent ingests scope, performs reconnaissance, builds an internal graph of assets, identities, and trust relationships. Memory persists across sessions.
Agent generates candidate attack paths ranked by feasibility and impact. Tree-search across exploit primitives. Chain-of-thought reasoning visible in audit log.
Agent runs primitives under tight RoE. Each result feeds back into the planner. Failed paths get re-weighted; successful paths get extended toward the objective.
Operators review the agent's full reasoning trace. Every decision is documented. Findings translated into engineering-ready remediation with auto-generated detections.
# reasoning loop — perceive → plan → act → reflect from stealthbyte.obsidian import Agent, ToolBelt async def run(target, objective, max_steps=256): agent = Agent(model="obsidian-3.2", temp=0.3) tools = ToolBelt([recon, scan, exploit, pivot]) for step in range(max_steps): observation = await agent.perceive(target) plan = await agent.plan(observation, objective) result = await tools.execute(plan.next_action) agent.remember(plan, result) if agent.objective_reached(objective): return agent.audit_trace
Plain-language account of what the agent did, what it found, and what it means. Written for executives who need to make AI-risk decisions.
Every thought, action, tool call, and observation the agent generated — fully reproducible. Engineering-ready and admissible.
For every finding, agent-generated detection rules tested against your SIEM. Sigma, Splunk SPL, KQL, and Elastic queries delivered ready-to-deploy.
Targeted adversarial test of one production LLM application. Prompt injection, jailbreaks, tool abuse, training data leakage. Full OWASP LLM Top 10.
Full-spectrum agent deployment against your environment. Network, identity, cloud, and AI surface — chained together by the agent over 4–8 weeks.
Persistent agent deployment. New attack paths surfaced weekly. Detection content updated continuously. Monthly executive briefings.
Agent deployments begin with a 90-minute scoping call under NDA. Briefings within 48 hours.
./deploy_obsidian.sh