Version bump 3.6.5 -> 3.6.6.
Local & uncensored models
- New `llamacpp:` provider (llama-server, OpenAI-compatible, localhost:8080,
no API key, CPU-only or GPU-offloaded). Override via LLAMACPP_BASE_URL;
model name is the loaded gguf (pass-through). 15 -> 16 providers.
- README: local/uncensored highlight, provider table + key-less note, badges.
Quality
- clippy clean under `-D warnings`: clamp(), sort_by_key(Reverse), struct-literal
init, too_many_arguments allows, scoped await_holding_lock on the REPL blocking
fallback (guard intentionally held across run().await), plus clippy --fix set.
CI
- examples/github-actions/ci.yml: cargo build/test/clippy -D warnings for the
neurosploit-rs workspace (template, kept out of .github/workflows).
Claude-Session: https://claude.ai/code/session_01QDses7zTSa9YF7pPRjphvh
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat: embed proof screenshots in reports, correlated to findings
Define a convention that ties each proof image to its vulnerability and
renders it in every report format.
- Finding gains `screenshots: Vec<String>` (paths relative to the run
workdir, e.g. evidence/<finding-id>-1.png).
- Exploit prompt injects an EVIDENCE SCREENSHOTS doctrine: agents save
proof PNGs into the run's absolute evidence/ dir named by a vuln slug,
and list them in the finding JSON `screenshots` array.
- collect_evidence() resolves whatever the agent captured (absolute,
workdir-relative, evidence/, /tmp basename), copies it to a stable
evidence/<finding-id>-N.png, and rewrites the field; unresolved refs
are dropped so a report never embeds a missing image.
- Typst (image()), HTML (<img>) and Markdown (![]) render each finding's
screenshots beside its evidence.
Tests: slugify + collect_evidence resolution/rename; verified a real PDF
compiles with an embedded image.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018BGLy4j5qsqqid6CoovowC
* feat: source-able env.sh to activate neurosploit in the current shell
Add env.sh: `source` it to export NEUROSPLOIT (binary path),
NEUROSPLOIT_BASE (agents base) and prepend the binary dir to PATH —
no reinstall or new terminal needed. Auto-detects the install/repo dir,
honors NEUROSPLOIT_DIR, idempotent. setup.sh now writes a ready env.sh
into the install dir and points users at `source <dir>/env.sh`.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018BGLy4j5qsqqid6CoovowC
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
- New agent account_registration_and_forms (+1 → 430): analyzes the app's forms
and self-registers a benign test account (curl or Playwright) to reach the
authenticated surface when no creds are given.
- Probe extracts form details (action/method/fields/kind/CSRF) so form analysis is
grounded; shown in the probe summary and recon JSON.
- Hard anti-flood guardrail in SAFETY_DOCTRINE + the agent: at most 2 accounts per
engagement, never loop/script/batch the register endpoint or flood the DB; reuse
the account made; a test needing many sign-ups is a lead, not mass-creation.
- Credential vault: engagement_ops directive tells agents to append created
accounts to <run-dir>/vault.jsonl; finish() consolidates to vault.json, masks
secrets in the report, and adds a 'Test accounts created (DELETE after)' cleanup
finding listing each account and how it was created.
- Finding tagging: new auth_context (authenticated/unauthenticated) and account
fields, rendered per-finding in the HTML report.
- Opt-in disposable email (off by default): /tempmail on + RunConfig.temp_email;
agents may use the free mail.tm API to read a registration confirmation code.
- Tests: parse_forms unit tests; docs updated (README/TUTORIAL/RELEASE), counts 430.
- Add 12 technique/scenario LLM red-team agents (AI category 18 → 30, total 429):
jailbreaks — AdvPrefix, PAIR, TAP, Crescendo, many-shot, persona/DAN,
encoding/obfuscation, refusal-suppression; prompt-injection scenarios — direct,
indirect (RAG/web/email/tool output), goal hijacking, tool/function-call abuse,
system-prompt/secret exfiltration. Each runs an attacker→LLM-judge loop
(baseline refusal → technique across variants → verdict), proving the bypass
with a benign, redacted receipt. Generated by scripts/build_llm_redteam_v365.py.
- Add REDTEAM_DOCTRINE and inject it into run_ai so every AI test follows the
baseline→technique→judge method across scenarios.
- Models: add Claude Opus 5 and Sonnet 5 (Anthropic) and a new Moonshot AI (Kimi)
provider with Kimi K3/K2 (moonshot:kimi-k3, MOONSHOT_API_KEY) — 15 providers.
- Docs: README/TUTORIAL/RELEASE — new AI/LLM red-team engagement mode + section,
model/env-key tables, agent-library counts (429), badges.
Also includes the v3.6.4 grounding fix (#33) landing on main.
The grounding gate ran in empirical mode for every engagement, demoting
white-box (and skills/n8n audit) findings that had passed the n-model vote
because a file:line code citation isn't raw tool output. Grounding is now
mode-aware:
- Symbolic (white-box SAST / skills): a file:line reference into the reviewed
source, or a quote of code present in it, is the receipt — no live target.
- Empirical (black-box / host / AI): evidence must resemble tool output (as before).
- Either (grey-box): a source citation OR a tool receipt grounds a finding.
The symbolic check runs against the reviewed source corpus (not the transcript)
and falls back to a structural file:line + quote check when the corpus is
unavailable. Adds unit tests incl. a regression test for #33.
- /continue (and /resume) now relaunch a recovered interrupted run on the same
target, carrying its findings forward and steering agents to widen coverage /
chain from them instead of re-reporting. Offer shown at launch; a fresh /run
supersedes it. Findings merge (dedup by title+endpoint) across both runs.
- Opening /results, /finding or /report while a run streams no longer corrupts
the terminal: live background output is paused for the picker (still captured
in /logs) and restored on exit, so Ctrl-C in a picker can't take the process
down mid-run.
- Drive `codex exec --json` and parse its JSONL event stream into the same
categorized live feed as Claude Code (exec/edit/tool/net/tokens), so recon and
exploitation are visible as each command runs instead of a silent black box.
- Fix the activity feed to keep per-agent tool events (commands, network, files,
findings) and only filter model reasoning + token telemetry, so /logs shows the
real command trail and /status 'last:' is a true sign-of-life.
- Surface failed internal commands as 'exec: (exit N)'; keep Codex auth/rate
detection from stderr.
Added the OpenAI GPT-5.6 line to the provider pool: gpt-5.6-sol (frontier/default),
gpt-5.6-terra (balanced), gpt-5.6-luna (fast/affordable). Version 3.6.0 -> 3.6.1.
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>
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>
Partial observability is now first-class:
- belief.rs — property-graph world model; nodes (host/service/vuln/exploit/cred)
carry a probability, not a boolean. Bayesian observation updates; per-node
Shannon entropy; mean-uncertainty + recon-frontier. Black-box = diffuse priors
that sharpen with observation; white-box collapses toward deterministic (MDP).
- pomdp.rs — value_of_information(), decide() (recon vs exploit falls out of
belief entropy), and may_assert() — the mathematical anti-hallucination gate:
no exploitability claim while the belief is diffuse (high entropy) → observe first.
- grounding.rs — verification engine, hard rule "no claim without a tool receipt":
empirical grounding for black-box (raw HTTP/OOB/error markers), symbolic for
white-box (file:line into reviewed source). Ungrounded claims demoted + flagged
receipt_missing (feeds future reward shaping).
- pipeline.finish(): grounding gate before reporting + belief-uncertainty readout.
- bump 3.5.0 → 3.5.1; README documents the v3.5.1 belief/grounding architecture
and the infra/bandit/reward roadmap.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Finding enriched with owasp / mitre / kill-chain stage / exploitability /
business_impact / chains_from (attack-path edges).
- attack_graph module: derive OWASP Top 10 + MITRE ATT&CK technique + kill-chain
stage from CWE (heuristic, no extra model call); render a Mermaid attack-path
flowchart (findings grouped by stage, explicit + implicit edges) and an ASCII
kill chain for the REPL.
- enrich() runs in finish() for every engagement.
- HTML report gains an "Attack Path & Kill Chain" section (Mermaid via CDN, dark)
plus a stage/sev/OWASP/MITRE/exploitability table.
- REPL print_findings shows the ASCII kill-chain + severity summary after a run.
- models: add GPT-5.5, GPT-5.4, GPT-5.4-mini, GPT-5.3-codex, GPT-5.2.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- New persistent interactive session (app/src/repl.rs), launched when run with no args:
banner, model selection, API-key config (/key) or subscription (/sub), then a live
session to set /target, /repo, /auth, and free-text /focus instructions (or just type
them) that STEER which agents run and how.
- Slash-commands: /model /providers /key /sub /target /repo /auth /focus /mcp /votes
/agents /show /run /quit (+ bare text = focus).
- RunConfig gains `instructions` and `auth`:
* instructions bias both LLM agent-selection and the heuristic (focus keywords →
injection/access-control/etc. agents get a strong boost)
* operator directives (focus + auth) injected into recon and exploit prompts so agents
test as an authenticated user and prioritise the requested vuln classes
- bump 3.4.1 → 3.5.0 (CLI, harness, reports, credits)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Task-based model ROUTER (recon/select prefer a fast model; exploit prefers primary; validate uses a different model than the finder)
- ReAct doctrine injected into exploit prompts (Thought→Action→Observation, token-efficient)
- Dedup: unique agents per run + findings deduped by CWE/endpoint/title (highest confidence kept)
- Token economy: recon blob capped for selector + per-agent context
- Configurable MCP: merge user mcp.servers.json into the pipeline's .mcp.json
- +54 white-box/code-analysis agents (NoSQLi, LDAP/XPath, JWT-none, Java/.NET/PHP/Go/Node/Python
specifics, SSTI, ReDoS, deserialization, etc.) → 303 agents total (78 code)
- Credits: Joas A Santos & Red Team Leaders (CLI banner, interactive header, HTML+Typst report)
- README: GitHub stars/forks badges, 60-second quick start, full API config steps, intuitive layout
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
