2 Commits

Author	SHA1	Message	Date
CyberSecurityUP	e4efa9bbb0	v3.5.2 — Exploitation Depth & Report Hygiene ... Distilled from reviewing real AI-pentest output that kept stopping at "exposed" instead of "exploited". Pure-additive, back-compatible. Behavior (injected into black/grey/chain exploit prompts via DEPTH_DOCTRINE): - Exposed → exploited: any info-disclosure / exposed service/WSDL / leaked credential|token / reachable dev host MUST be used before it's a finding; otherwise it's a lead, not a confirmed High/Critical. - Chain across modules: reuse obtained session/JWT/cookie/credential and pivot to IDOR/privesc/exfil; report the chain, not isolated parts. - Decode & fingerprint → CVE; audit tokens (alg-confusion/none/kid/JWKS, weak HS256 secret cracking, lifecycle). Deterministic post-pass (new crates/harness/src/hygiene.rs, wired into finish()): - calibrate severity to PROVEN impact — unproven High/Critical (hedged, no payload, thin evidence) capped to Medium and re-titled "(potential)"; - depth_audit — flag exposures on a host with no real exploit; - hygiene_summary — advise consolidating hygiene classes repeated across assets. Unit tests cover calibration + depth audit. 5 new doctrine meta-agents (scripts/build_methodology_v352.py → agents_md/meta/): exploit_depth_doctrine, finding_chainer, artifact_decoder, token_auditor, report_calibrator (meta 17→22, total 343→348). Version bumped 3.5.1 → 3.5.2 across crates/app/installers/docs; RELEASE/README updated. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-26 11:31:11 -03:00
CyberSecurityUP	55af0d4634	NeuroSploit v3.3.0 — Autonomous MD-Agent Engine ... Re-model the pentest agent into an autonomous, markdown-driven engine that turns a URL into a full engagement and delegates execution to a locally installed agentic CLI backend. Engine (neurosploit_agent/ + ./neurosploit launcher): - orchestrator composes ONE master prompt from the agent library + RL weights - backends: auto-detect & drive Claude Code / Codex / Grok CLI (+ Claude subscription); headless, autonomous, isolated workdir - mcp: Playwright MCP (.mcp.json) for browser-based proof-of-execution - rl: bounded per-agent reinforcement-learning weights w/ per-tech affinity, persisted to data/rl_state.json - models: latest registry incl. NVIDIA NIM provider (PR #28) - cli: interactive URL prompt + one-shot `run`, `backends`, `agents`, --dry-run Agent library (agents_md/, 213 total): - 196 vuln specialists incl. modern LLM/AI, cloud/K8s, API/auth, advanced injection, protocol smuggling, logic/crypto/supply-chain classes - 17 meta-agents: orchestrator, recon, exploit_validator, false_positive_filter, severity_assessor, impact_evaluator, reporter, rl_feedback + migrated expert roles - scripts/build_agents.py data-driven builder; REGISTRY.md index Docs: rewritten README.md, v3.3.0 RELEASE.md, .env.example (NVIDIA NIM, xAI, engine vars). Retire legacy Python orchestration (neurosploit.py + agent classes) to legacy/. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-14 20:57:38 -03:00