2 Commits

Include renames

Author	SHA1	Message	Date
CyberSecurityUPandClaude Opus 4.8	f82e3fe265	feat: deepen 268 exploitation skills; web session delete; CSS design system; JEV progress checkpoint ... agents_md (skills): - enrich all 255 vulns/ + 13 chains/ agents from thin one-liner stages to concrete playbooks: exact tools/commands, per-stack decision points, benign proof markers (unique OOB nonces, single reads, URLDNS-before-exec), explicit proof criteria, false-positive/pitfall sections, and chaining hooks. Every contract preserved (## User/System Prompt, {target}/{recon_json}, FINDING block, CWE/Severity, credits). avg 37->53 lines; loader parses all 449. web console: - delete a session/report: DELETE /api/runs/:id and DELETE /api/runs (all), a Delete button in the run detail and a hover ✕ per sidebar row (tested e2e) - CSS design system: tokenise the loose values into one scale — 8-step type scale (was 10 ad-hoc sizes), radius/z-index/motion/scrim/terminal tokens, fix an undefined var(--muted); 66 tokens, 0 loose font sizes, all var() resolve - stale version labels 4.0.0/4.2.0 -> 4.2.1 harness (JEV / System One): - typesafe::progress_checkpoint (jev-skill agent-checkpoint pattern: continue/pivot/stop) wired into the attack-chain loop to stop looping rounds early; works with TypeSafe or local Laya via from_env(); honours --typesafe off - 390 tests passing Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-09-26 16:25:58 -03:00
CyberSecurityUPandClaude Opus 4.8	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