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Hunt Sqli

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Encod3d-Sec
hunt-sqli

SQLi and NoSQLi hunting - error-based, boolean-blind, time-based, UNION, NoSQL operator injection. sqlmap automation after manual confirmation. Wiki-first, FIND schema output.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namehunt-sqli
Stars
334
Forks
48
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Encod3d-Sec on GitHub. Read the source before you install it.

Installation

Install the Hunt Sqli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-sqli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Encod3d-Sec/TORCH.git /tmp/TORCH
mkdir -p .claude/skills
cp -r /tmp/TORCH/skills/hunt/hunt-sqli .claude/skills/hunt-sqli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Sqli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hunt Sqli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hunt Sqli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hunt: SQL Injection

Assumes hunt-core for the scope gate, two-account rule, confirmation gate, enumeration limits, stop conditions, wiki protocol, FIND output, and Deadends. Do not re-derive any of that here.

Wiki

qmd_query "SQL injection SQLi NoSQL union boolean-blind time-based error-based" via wiki-search MCP

Hub: [[web-moc]] (live web index). Primary page: [[sql-injection]]. Payload arsenal: wiki/payloads/sqli.md. Anchors: [[nosql-injection]], [[orm-injection]].

Confirmation gate

NOT confirmation: a generic 500; a WAF block page or a "SQL injection detected" string (that is the app's filter firing, not the database); a reflected error string you have not proved originates from the DB; a single non-repeatable slow response; a boolean or time inference with no differential (no clean baseline-vs-injected delta); the payload echoed back from your own request; any verdict read off a response body without a clean-session re-verify.

IS confirmation: a DB error whose text is demonstrably from the engine (extractvalue/updatexml output, a driver error naming the DBMS); UNION or error-based output that returns real DB content; a boolean oracle that flips TRUE/FALSE consistently across probes; a time delta that reproduces on repeat (baseline vs SLEEP(5)/pg_sleep(5)/WAITFOR, re-run); or an out-of-band DNS/HTTP hit to your unique Collaborator/interactsh subdomain. Re-verify in a CLEAN session every time; on a login-redirect oracle clear the session cookie between probes (a stale authenticated session reads as always-true).

Out-of-band (blind SQLi via DNS/HTTP). When you plant a blind/OOB payload, append a row to targets/<eng>/oob.md: | <token> | <sink url+param> | sqli | <date> | waiting | | (columns: token | sink | class | planted | status | source, token = your unique Collaborator/interactsh label). Exfil sinks: MySQL LOAD_FILE/UNC path, MSSQL xp_dirtree, Oracle UTL_HTTP. The recon-capture hook auto-correlates the callback to flip the row to HIT and SessionStart surfaces HITs; a HIT row is the gate to scaffold the FIND. Do NOT claim a blind SQLi without a HIT row.

Attack surface

Highest-value injection points first:

  • Authentication / login - a bypass here is auth bypass; the login-redirect oracle (below) turns even a filtered, output-less login into a blind extractor.
  • Search / filter / sort / report params - ?q=, ?category=, ?sort=&order= (ORDER BY injection), ?start_date=; user input lands straight in a WHERE/ORDER BY.
  • Anything that reflects query output or a DB error - error-based and UNION need a reflection sink; if the page you are hitting reflects nothing, test on one that does.
  • JSON bodies with nested objects - operator injection -> NoSQL ({"$gt":""}, [$ne]).
  • Second-order sinks - a value stored safely at register/login but rendered unsafely elsewhere (profile, dashboard, "last logins").
  • Headers, cookies, path segments - the least-tested vectors; ORDER BY / LIMIT clauses frequently take raw input.

URL patterns:

/search?q=  /filter?category=  /sort?by=&order=  /report?start_date=
/api/v1/items?id=  /index.php?id=  /gallery?album_id=  ?page=&limit=

Fingerprint the DB: PHP + Apache -> MySQL; Express + MongoDB -> NoSQL; application/json with nested objects -> potential NoSQL.

Methodology

Order: in-band before blind. Try error-based and UNION (direct data/errors) BEFORE boolean/time-based - blind is slow and easily masked (e.g. an anti-bruteforce SLEEP() on the login page adds a constant delay that hides your injected SLEEP). UNION/error need a place where query output or a DB error is REFLECTED; if the page you are hitting reflects nothing, test the SQLi on a different page that does.

  1. Enumerate all input vectors (GET, POST, JSON body, headers, cookies, path segments).
  2. Baseline the response (length, status, time).
  3. Error probes in every quote context - ' " ` ') ")) and numeric/no-quote. A ' doing nothing does NOT mean safe: the sink may be double-quoted (WHERE x="$v"). Watch for a reflected DB error or any length change. On a numeric-looking id param, test the NUMERIC context FIRST with a boolean pair - id=1 AND 1=1 (row shows) vs id=1 AND 1=2 (row gone). That one request pair settles the context before you burn turns theorizing quote/LIKE breakouts; a ' that only errors can be a WAF/troll page, not the real query shape.
  4. In-band first: error-based (extractvalue/updatexml) and UNION (ORDER BY for col count, then UNION SELECT). Mind display truncation when sizing extracted chunks.
  5. Only then blind: boolean (AND 1=1 vs AND 1=2) then time (SLEEP(5)/pg_sleep(5)/WAITFOR).
  6. Second-order: if login/register is parameterized, the SAME stored value may be unsafe on another page (profile/dashboard/"last logins") OR simply wherever the app echoes your stored name back - a greeting, a "Welcome X", a counter/result line. Register the payload as the username, log in, then load the page that renders it - and READ THE WHOLE reflected block, not a keyword grep: the phrasing can shift (singular "1 word" vs plural "N words") and hide the dumped data. This class is rare and under-tested, so it is easy to walk past - always try it before concluding a parameterized login is safe. See the PROCESSLIST section below.
  7. NoSQL (MongoDB): replace value with {"$gt": ""} or param[$ne]=invalid.
  8. Tool-first for the dump, always. Confirm manually in 1-2 requests only (e.g. an arithmetic tell: id=2-1 evaluates to row 1 = injection proven) - the NEXT step is sqlmap, never a hand-rolled group_concat/LIMIT paging loop to extract the data yourself:
bash
# ONE confirming curl (baseline vs injected); then hand off to sqlmap, don't hand-loop probes
curl -o /dev/null -s -w "%{time_total}\n" "https://target.com/search?q=test' AND SLEEP(5)-- -"

# sqlmap owns the rest: DBMS fingerprint, WAF bypass, enumeration, extraction
sqlmap -u "https://target.com/search?q=test" --level=3 --risk=2 --batch --dbs

Keep exactly ONE manual curl in the writeup as the confirming PoC; sqlmap owns enumeration.

Anti-automation signal -> do NOT run sqlmap at all (stay manual + serial). A taunt string ("try sqlmap", "I dare you"), a request-rate-limiter / lockout, a char-blacklist WAF, or intermittent empty/ban responses after a burst = the box is BUILT to defeat sqlmap. Its detection burst just trips the limiter, and every response after that measures the ban, not the app (easily misread as "server load / worker starvation"). Injection is already manually confirmed, so sqlmap adds nothing here: extract by hand, ONE serial request at a time. Bypass the char blacklist with functions + hex literals (database(), 0x7573657273 for a blocked table/column name) instead of quotes/comments, and put the data in a column that reflects.

Rate-sensitive / fragile / worker-starving target -> sqlmap's detection burst trips HTTP 000. Run it gentle: --delay 2 --timeout 40. Since injection was already manually confirmed, let sqlmap resume from its own session (same output dir/target) instead of restarting - it skips re-detection and goes straight to a fast UNION dump rather than re-probing the whole boundary. Scope sqlmap to PROVING the vuln, not mass-exfiltrating records. An automated table dump is data extraction and falls under the hunt-core enumeration limits: confirm + fingerprint + a bounded sample (--dump with --start 1 --stop 5), never --dump-all. Sample of 5, ceiling 20 with operator approval, 0 under no_bruteforce. 9. Escalate impact (scoped to proof, not mass extraction): UNION extraction, INFORMATION_SCHEMA for schema shape, file read/write if perms allow. See Chaining below. 10. Document: Burp Repeater screenshot + sqlmap output + a non-sensitive data sample. Push the confirming request into Burp Repeater (Skill(hunt-burp) / capture.sh burp) so the operator can replay it; use Collaborator for the OOB payloads above.

Distill when confirmed - reusable NoSQL/ORM bypass, GENERIC, no client host: python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/web/sql-injection.md

Key Payloads

sql
-- Error probes
' '' ` ') ")) ' OR '1'='1 admin'--

-- Time-based (MySQL)
' AND SLEEP(5)--

-- Time-based (MSSQL)
'; WAITFOR DELAY '0:0:5'--

-- UNION (find column count first)
' ORDER BY 1-- ' ORDER BY 10--
' UNION SELECT NULL,NULL,NULL--
' UNION SELECT 1,database(),3--

-- NoSQL (MongoDB JSON body)
{"username": {"$gt": ""}, "password": {"$gt": ""}}
{"username": {"$regex": ".*"}}

-- NoSQL (query string)
username[$ne]=invalid&password[$ne]=invalid

Chaining

Once you have a working injection, escalate along these edges (keep extraction scoped to proof):

  • File read - MySQL LOAD_FILE('/etc/passwd'), MSSQL OPENROWSET, Oracle UTL_FILE; the OOB DNS-exfil sinks above double as blind file read.
  • File write -> RCE - MySQL INTO OUTFILE/DUMPFILE a web-shell into the docroot; MSSQL xp_cmdshell; Postgres COPY ... TO PROGRAM; stacked queries where the driver allows them. Hand off to hunt-rce once you have command execution.
  • Credential reuse - hashes/plaintext pulled from the DB (see PROCESSLIST below) are frequently reused for SSH, not the web login. Crack, then pivot; hand off to hunt-auth for the ATO.
  • Dumped table -> targeted crack wordlist. A dumped user/credential table (even a different app's plaintext passwords) is a high-value CUSTOM wordlist for cracking a related hash elsewhere (e.g. a CMS admin hash) - beats rockyou. See [[password-attacks]].

Evasion

A WAF block or a "SQL injection detected" string is NOT "closed" - it is a filter to map and bypass, not a fix. Vary the payload: inline comments (/**/, -- - vs #), case (SeLeCt), encoding (URL/double-URL/hex, but note some filters block 0x), whitespace alternatives, AND logic instead of OR, CASE WHEN instead of IF. The keyword-filter / login-redirect-oracle section below is the worked example.

Second-order + PROCESSLIST credential capture

When the app hashes the password inside the SQL (... AND pass=md5("PLAINTEXT")) the plaintext sits in the live query. If a bot/admin logs in periodically (often held a few seconds by an anti-bruteforce SLEEP()), read it via the SQLi - the app connects as the same DB user, so you see its threads even without global PROCESS priv:

sql
" UNION SELECT 1,SUBSTRING((SELECT info FROM information_schema.PROCESSLIST
  WHERE id=(SELECT MIN(id) FROM information_schema.PROCESSLIST)),POS,16)-- -   -- POS=1,17,33,...

INFO is non-NULL only while running -> poll fast, timed to the bot. Output truncates to the sink's display width -> register one second-order account per N-char window, log each in, hammer the rendering page with all sessions during the bot's window, concatenate the blocks. Recovered creds are often reused for SSH, not the web login. Full writeup: [[sql-injection]].

Cracking recovered hashes (try easy first)

  1. Unsalted MD5/SHA1 -> online lookup first (CrackStation 190GB table, hashes.com) - instant for leaked/common passwords that rockyou+rules miss. (Note: needs a browser/captcha; if the tooling host has no internet, do it from a box that does.)
  2. Then local: hashcat -m 0 h /usr/share/wordlists/rockyou.txt -r best64, then john.
  3. A hash that resists all of the above on a "hard" box may be a decoy/rabbit-hole - pivot (e.g. PROCESSLIST capture above) instead of grinding. A 0e... MD5 is only a magic hash for PHP == if EVERY char after 0e is a digit.

App keyword-filter + login redirect oracle (sqlmap blind-spot)

App rolls its own preg_match blacklist (returns a fixed "SQL Injection detected" string). Map it one token at a time, then bypass: AND logic not OR, -- - not /**/, quoted literals not 0x hex, CASE WHEN not IFNULL. If the post-login page is static, the login redirect is the oracle: user' AND 1=1-- - -> 302 (TRUE), AND 1=2 -> 200 (FALSE). sqlmap usually FAILS here (hex-encodes data as 0x -> blocked; follows the 302 -> diff confusion), so hand-roll a boolean extractor and clear the session cookie every probe (a login oracle otherwise stays authenticated -> always-true). Don't name the script enum.py (shadows stdlib). See [[sql-injection]].

Severity

Confirmed = data extracted, a DB-sourced error/UNION reflection, a consistent boolean oracle, a reproducible time delta on repeat, or an OOB HIT.

DemonstratedTypical
Full DB dump, or RCE via xp_cmdshell / INTO OUTFILE web-shellcritical
Arbitrary data extraction (auth bypass, cross-table read, credentials)high
Blind boolean/time-based only, no data pulledmedium

Rate on demonstrated impact per hunt-core, not the theoretical maximum.

Deadends

Append: - [ ] SQLi on <host> param <x> -- all probes 200/same-length, no time delta or DB error;
              tried quote contexts / UNION / boolean / time / OOB, WAF filter mapped and bypassed

Record what you tried, not just that it failed. The next pass needs to know the boundary.

Frequently asked questions

What does the Hunt Sqli AI skill do?

SQLi and NoSQLi hunting - error-based, boolean-blind, time-based, UNION, NoSQL operator injection. sqlmap automation after manual confirmation. Wiki-first, FIND schema output.

Why use Hunt Sqli on TypingMind?

Because you install it once and use it with any model. Hunt Sqli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hunt Sqli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-sqli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Sqli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hunt Sqli?

As many as you like. As long as a model supports skills, you can use Hunt Sqli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hunt Sqli AI skill free?

Yes. It is published on GitHub by Encod3d-Sec under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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