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ARES — Autonomous red-team engagement platform with MITRE ATT&CK module orchestration, DAG attack-path solving, OPSEC controls, encrypted credential vault, and multi-format reporting. | Kitploit
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GitHubmafifrizi/ares

ARES

Autonomous red-team engagement platform with MITRE ATT&CK module orchestration, DAG attack-path solving, OPSEC controls, encrypted credential vault, and multi-format reporting.

5821011372 days agoReviewed by Kitploit

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Operator-Directed Red Team Orchestration & Continuous Security Validation Platform

The open-core platform empowering enterprise red teams, MSSPs, and security operations centers to execute targeted offensive engagements, discover deterministic attack paths, and continuously validate defensive posture with deterministic scope enforcement and fail-closed isolation controls.


Version Python FastAPI React MITRE Security License Tests Suite GitHub Sponsor Saweria



Mafifrizi%2FARES | Trendshift



Platform Showcase • Why ARES? • Competitive Matrix • Architecture • MITRE Matrix • Quickstart • Sponsorship • Credits • Zero-Trust Security • Documentation


Executive Summary

Traditional penetration testing is fundamentally flawed: it is expensive, episodic, point-in-time, and leaves organizations blind to newly introduced misconfigurations and emerging adversary tradecraft. Meanwhile, automated vulnerability scanners overwhelm SOC teams with thousands of hypothetical CVEs without demonstrating exploitability or multi-stage lateral attack paths.

ARES bridges this gap. Built from the ground up for modern enterprise infrastructure, ARES delivers an operator-directed red team engagement platform that models real-world threat actors. By combining deterministic application-layer ScopeGuard fail-closed enforcement, adaptive OPSEC noise profiling, an interactive directed acyclic graph (DAG) attack solver, and 66 active production execution modules (70 total catalogued) covering 60+ mapped adversary techniques across MITRE ATT&CK, ARES allows security teams to prove vulnerability exploitability, locate shortest compromise paths to Active Directory Crown Jewels, and generate executive-ready deliverables with zero operational downtime.


Why Enterprises Choose ARES

                                    THE ARES ADVANTAGE
 ┌─────────────────────────┐   ┌─────────────────────────┐   ┌─────────────────────────┐
 │   STRICT SCOPE ENCLAVE  │   │  DIRECTED ATTACK ENGINE │   │ ZERO-TRUST ARCHITECTURE │
 │ Fail-closed ScopeGuard  │   │ Computes shortest paths │   │ Memory-only JWT tokens, │
 │ & Egress Scope Wall     │───│ to Domain Admins & Crown│───│ AES-256-GCM AEAD vault, │
 │ prevents out-of-scope.  │   │ Jewels deterministically│   │ strict HMAC-CSRF checks.│
 └─────────────────────────┘   └─────────────────────────┘   └─────────────────────────┘
                                            │
                                            ▼
                               ┌─────────────────────────┐
                               │ INSTANT EXECUTIVE SUITE │
                               │ Branded PDF, HTML, JSON │
                               │ deliverables with zero  │
                               │ native GTK dependencies │
                               └─────────────────────────┘

Core Value Pillars

  1. Zero-Collateral Scope Governance (Dual-Layer Scope Firewall): Every campaign executes inside a hardened, isolated boundary. ARES pairs deterministic application-layer ScopeGuard pre-checks with a dual-layer Scope Firewall & Egress Filter that integrates OS-level packet filtering (Windows Defender Firewall / Linux Netfilter when elevated) with transport-level socket interception (socket.connect, socket.sendto, asyncio.create_connection) in Python user space. Guarantees fail-closed enforcement so zero offensive packets reach unapproved IP addresses or subnets.
  2. Goal-Directed Attack Planning: Operators set high-level objectives (e.g. domain_admin, full_compromise, cloud_audit), and the ARES decision engine synthesizes multi-stage execution paths using graph heuristics and optional LLM agents (Claude / OpenAI / local Ollama) under explicit operator authorization.
  3. Multi-Vector Attack Graph (DAG): Ingest BloodHound collections or map active domain sessions in real time. The built-in graph engine calculates shortest attack paths, highlights chokepoints, and simulates lateral pivot feasibility.
  4. Defense-in-Depth Security Model: Engineered for zero-trust environments. Database-authoritative sessions, memory-only short-lived JWT access tokens, HttpOnly rotating refresh credentials, and AES-256-GCM AEAD encrypted local vaults (PBKDF2-HMAC-SHA256 600k iterations) protect harvested hashes and sensitive client evidence.
  5. Automated Deliverables Pipeline: One-click generation of branded, audit-ready compliance deliverables in PDF, HTML, Markdown, and JSON formats. Features automated headless browser PDF rendering on Windows/Linux without external GTK dependencies.

Market Comparison Matrix

Operational CapabilityTraditional Manual PentestLegacy Vulnerability ScannersARES Orchestration Platform
Testing FrequencyAnnual / Semi-AnnualScheduled Daily / WeeklyContinuous / On-Demand
Exploitability ValidationManual & Labor IntensiveTheoretical CVE Matching (No Validation)Deterministic Multi-Stage Proof
Multi-Hop Attack PathsManual DrawingNoneReal-Time Interactive DAG
Scope Enclave & Egress GuardOperator Discipline OnlyNetwork Firewalls OnlyFail-Closed Dual-Layer ScopeGuard & OS/Transport Firewall
Active Directory Lateral PathsSlow Script ExecutionNo Active PathsNative BloodHound, Kerberos Suite & Linux AD RFC Track
Credential Security & StorageLoose Flat Files / CleartextVulnerability LogsAES-256-GCM Authenticated Vault
OPSEC & Telemetry ThrottlingManual Jitter ScriptsHigh Network NoiseAdaptive Noise Profiles & Governor
Delivery Time for Reports1-2 Weeks Post-EngagementRaw Data DumpsInstant Multi-Format Artifacts
Deployment FootprintExternal ConsultantsBulky Cloud AgentsAir-Gapped Local / Self-Hosted

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