Commit Graph

8 Commits

Author SHA1 Message Date
CyberSecurityUP 64decada3e v3.5.3 — Integrations (GitHub · GitLab · Jira)
New harness module `integrations` (+ app commands) wiring NeuroSploit into the
SDLC. Config persists per-project to .neurosploit/integrations.json; secrets are
NEVER stored — only the env-var name is saved, values read from the environment.

GitHub:
- private-repo clone (token injected into the clone URL for whitebox/greybox/tui)
- `neurosploit pr <owner/repo> <n>`: clone the PR head (refs/pull/N/head),
  white-box review, optional `--comment` (PR summary) and `--jira` (cards)
- `neurosploit watch <owner/repo> --branch --interval`: re-review on each new commit
GitLab:
- private-repo clone (oauth2 token) for whitebox/greybox (gitlab.com or self-hosted)
Jira:
- `--jira` on any engagement opens one card per finding (REST /issue, basic auth)

Control:
- `/integrations` (REPL): show · enable/disable · setup jira|gitlab|github
- `neurosploit integrations [show|enable|disable] [github|gitlab|jira]` (CLI)

Docs: README "Integrations" section + new TUTORIAL-INTEGRATION.md (per-tool setup,
scopes, recipes, troubleshooting). Version bumped 3.5.2 → 3.5.3.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-27 01:56:49 -03:00
CyberSecurityUP d957429c09 feat(models): add Azure OpenAI provider + GOOGLE_API_KEY alias for Gemini
Resolves the only two open issues that still apply to the Rust build:
- #21 Azure OpenAI: new `azure` provider (OpenAI-compatible). Endpoint comes
  from AZURE_OPENAI_ENDPOINT, api-version from AZURE_OPENAI_API_VERSION
  (default 2024-10-21); the model name is the Azure deployment; auth uses the
  `api-key` header instead of Bearer. Use `--model azure:<deployment>`.
- #25 Gemini key confusion: GEMINI_API_KEY now also accepts GOOGLE_API_KEY
  (Google's standard env var) as an alias; local providers (ollama/litellm)
  require no key. .env.example documents both.

Kept under the v3.5.2 line (additive provider support).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-26 14:17:25 -03:00
CyberSecurityUP 761d3df444 feat: whitebox/greybox/repl accept a GitHub URL (auto-clone)
`whitebox <arg>`, `greybox --repo <arg>`, `tui --repo`, and the REPL `/repo`
now accept a git URL (https://github.com/owner/repo[.git], git@…, ssh://, *.git)
or an `owner/repo` shorthand. A new resolve_source() shallow-clones it into
<base>/repos/<name> (cached, .gitignored) and reviews it; existing local paths
are used unchanged. Works identically with API-key (--model) and --subscription.

Verified: `neurosploit whitebox https://github.com/digininja/DVWA --offline`
clones DVWA and runs the 78 code agents over 120KB of source.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-26 13:52:51 -03:00
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 ac84db024c docs: add v3.5.1 release notes to RELEASE.md
Prepend the 3.5.x entry: interactive REPL, POMDP belief/grounding, infra/host
(SSH + Windows/AD), attack-chain & app-stack/CVE agents, LiteLLM, Mission-Control
TUI, structured Typst report, and the new run control (background /run, 3-way
/stop, crash recovery, pause-on-quota /continue).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-25 09:28:16 -03:00
CyberSecurityUP 56d3f0c723 NeuroSploit v3.4.0 — Rust multi-model harness + Axum dashboard
New cargo workspace `neurosploit-rs/` (single `neurosploit` binary):

harness crate:
- models.rs: 11 OpenAI-compatible providers / 31 models (Claude, GPT, Grok,
  NVIDIA NIM, DeepSeek, Mistral, Qwen, Groq, Together, OpenRouter, Ollama)
- pool.rs: ModelPool with bounded concurrency, provider failover, and N-model
  validator voting (the panel doubles as the jury)
- agents.rs: loads the existing agents_md/ library (213 agents)
- pipeline.rs: recon → parallel exploit (semaphore-bounded) → N-model
  adversarial vote → score; streams live progress over a channel
- report.rs: HTML report
- tokio + reqwest(rustls); offline mode runs the pipeline without API keys

app binary:
- clap CLI: serve | run | agents | models  (run supports --model x N, --vote-n,
  --max-agents, --offline)
- axum web dashboard with multi-model panel, live console, findings, agent
  browser, embedded report; single binary serves the SPA (no npm/build)

Verified: cargo build clean; agents/models/offline-run CLI; server endpoints
(/api/info, /api/run lifecycle, /report); dashboard + live run in Playwright.

Docs: README v3.4.0 callout + RELEASE.md notes. target/ gitignored.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-21 19:58:43 -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
CyberSecurityUP e0935793c5 NeuroSploit v3.2 - Autonomous AI Penetration Testing Platform
116 modules | 100 vuln types | 18 API routes | 18 frontend pages

Major features:
- VulnEngine: 100 vuln types, 526+ payloads, 12 testers, anti-hallucination prompts
- Autonomous Agent: 3-stream auto pentest, multi-session (5 concurrent), pause/resume/stop
- CLI Agent: Claude Code / Gemini CLI / Codex CLI inside Kali containers
- Validation Pipeline: negative controls, proof of execution, confidence scoring, judge
- AI Reasoning: ReACT engine, token budget, endpoint classifier, CVE hunter, deep recon
- Multi-Agent: 5 specialists + orchestrator + researcher AI + vuln type agents
- RAG System: BM25/TF-IDF/ChromaDB vectorstore, few-shot, reasoning templates
- Smart Router: 20 providers (8 CLI OAuth + 12 API), tier failover, token refresh
- Kali Sandbox: container-per-scan, 56 tools, VPN support, on-demand install
- Full IA Testing: methodology-driven comprehensive pentest sessions
- Notifications: Discord, Telegram, WhatsApp/Twilio multi-channel alerts
- Frontend: React/TypeScript with 18 pages, real-time WebSocket updates
2026-02-22 17:59:28 -03:00