Agentic Red-Team Simulator · Hacking Project

Continuously and safely emulate adaptive, multi-stage adversaries in a controlled range to measure and improve detection coverage.

**The problem**
Traditional adversary emulation is manual, expensive, and episodic, so defenses are validated only during scheduled engagements. As organizations adopt AI agents, they also need to test how autonomous, multi-step adversaries behave against their controls. Security teams lack an affordable, repeatable way to run realistic, ATT&CK-aligned attack campaigns continuously and safely.

**What you'll build**
Build an agentic red-team simulator in which an LLM-driven planner orchestrates multi-stage attack campaigns (recon, initial access, lateral movement, exfiltration) against a controlled lab or purple-team range. The agent selects techniques from an ATT&CK-mapped playbook, adapts to observed defenses, and produces a full campaign timeline with detections triggered and gaps found. It runs only in authorized environments with strict guardrails, producing a repeatable, measurable adversary-emulation capability that feeds directly into detection engineering.

**Objective** — Continuously and safely emulate adaptive, multi-stage adversaries in a controlled range to measure and improve detection coverage.

**Expected outcome** — An agentic adversary-emulation tool that runs ATT&CK-mapped campaigns in a lab and outputs a campaign report with detection coverage and gap analysis.

**Difficulty** — Expert — combines adversary emulation, autonomous agent orchestration, safe range design, and detection measurement.

**Primary category** — Adversary Emulation / Purple Teaming; Secondary = Offensive Security Automation, AI/ML Security, Detection Engineering

**Security domains** — Primary = Red Team; Secondary = Purple Team, AI Security, Blue Team

**Technical focus** — Autonomous attack orchestration, adversary emulation, ATT&CK technique chaining, breach-and-attack simulation, agent planning/tool-use, detection coverage measurement, cyber range design, campaign reporting, safe-execution guardrails

**Core technologies** — Languages: Python, PowerShell, Bash; Frameworks/Libraries: LLM agent framework (LangGraph), MITRE Caldera/Atomic Red Team, pydantic; Security Tools: Caldera, Atomic Red Team, Sliver/C2 in lab, Sigma; Cloud/Infra: isolated cyber range, Docker/K8s, SIEM (Elastic/Splunk) for detection; AI/Data: LLM planner with tool-use, technique knowledge base; Databases: PostgreSQL, graph store for campaign paths

**Where it's valued** — Red/purple teams, MSSPs, breach-and-attack-simulation vendors, security research, enterprise SOCs

**Careers it builds toward** — Red Team Operator, Purple Team Engineer, Adversary Emulation Specialist, Detection Engineer, Security Automation Engineer

**Framework alignment**
• MITRE ATT&CK: Full kill-chain coverage — Reconnaissance (T1595), Initial Access (T1190, T1566), Execution (T1059), Lateral Movement (T1021), Exfiltration (T1041); campaigns are authored directly against ATT&CK tactics/techniques. MITRE ATLAS AML.T0040 covers the AI-driven offense dimension
• NIST CSF 2.0: Detect (primary via coverage measurement), Identify, Protect, Respond
• OWASP: N/A for infra emulation; LLM Top 10 applies to securing the agent orchestrator itself
• CIS Controls v8: 13 (Network Monitoring & Defense), 17 (Incident Response Management), 18 (Penetration Testing)

**Innovation** — Startup Potential — autonomous, adaptive adversary emulation is a natural evolution of the breach-and-attack-simulation market and agentic orchestration is a strong differentiator. Scores: Innovation 8/10, Startup 8/10, Enterprise-Readiness 6/10, Patent = Possible.