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🤖 THE FIRST AI-POWERED SQL INJECTION TESTING TOOL 🤖
Revolutionary • Intelligent • Autonomous • Dangerous
═══════════════════════════════════════════════════════════════════════════════════════
⚠️ DESIGN SPEC: This tool WILL combine AI intelligence with comprehensive exploitation ⚠️
⚡ Planned Agentic Mode: LLM will autonomously discover, test, and exploit vulnerabilities
🔥 When built: Use only with explicit authorization on systems you own or have permission to test
Revolutionary. Intelligent. Dangerous.
This is the design & technical specification for blackSQL 2.0 — the world's first LLM-powered SQL injection tool. By combining Rust performance with AI intelligence, blackSQL 2.0 transforms SQL injection testing from static pattern matching into adaptive, intelligent security research.
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blackSQL 2.0 is the first SQLi tool to integrate AI/LLM capabilities, setting it apart from every existing tool:
🧠 AI-Powered Intelligence
- LLM generates context-aware payloads based on target tech stack and responses
- Interprets database errors intelligently, reducing false positives dramatically
- Provides human-readable narrative reports with remediation advice
- Agentic mode: LLM autonomously decides next testing steps, calls tools, adapts strategy
⚡ Rust Performance
- Single binary, no runtime dependencies
- Concurrent execution, native threads
- 10-100× faster than Python equivalents on multi-URL scans
🎯 Comprehensive Coverage
- All injection points: GET, POST, headers, JSON, cookies
- Multi-URL scanning, crawling, API testing
- Four core techniques + optional OOB and second-order SQLi
💀 Controlled Exploitation
- Explicit scan vs exploit modes with safety guardrails
- Interactive SQL shell, database enumeration, data dumping
- Hash extraction, privilege checks, file read/write (behind confirmations)
📊 Modern Reporting
- Markdown, HTML, SARIF (GitHub CodeQL, CI/CD integration)
- LLM-generated narrative reports with severity and remediation
- Evidence capture for compliance
No other SQLi tool combines AI intelligence with this level of exploitation capability and modern DevSecOps integration.
Current (Python): Traditional static-payload SQLi scanner. Detects four types of SQLi (error-, boolean-, time-, union-based), fingerprints databases, bypasses WAFs, outputs JSON/CSV. Proven, tested, works well for basic detection.
New (Rust + AI): The next generation. Same reliable detection core, reimagined with:
- 🤖 LLM/AI integration — Ollama-powered payload generation, response interpretation, agentic testing
- ⚡ Rust speed — single binary, concurrent, 10-100× faster on scale
- 💀 Explicit exploitation — scan vs exploit modes; interactive shell; dump to file; safe PoC
- 🌐 Broad coverage — multi-URL, crawl, headers/JSON/methods, API-ready
- 📈 Modern reports — Markdown/HTML/SARIF, CI exit codes, evidence trails
- 🛡️ Safety helpers — optional scope/rate limiting/confirmation/audit for responsible use
Purpose: For authorized security testing by researchers and pentesters. Like any Kali Linux tool, it's powerful and unrestricted — use responsibly and only on systems you own or have permission to test.
╔══════════════════════════════════════════════════════════════════════════════╗
║ ║
║ ⚠️ ⚠️ ⚠️ WHY THIS IS DANGEROUS ⚠️ ⚠️ ⚠️ ║
║ ║
║ 🔥 AUTONOMOUS AI • MACHINE SPEED • FULL EXPLOITATION 🔥 ║
║ ║
╚══════════════════════════════════════════════════════════════════════════════╝
blackSQL 2.0 represents a significant escalation in SQL injection testing capabilities. Here's what makes it exceptionally powerful:
Traditional tools follow fixed patterns. blackSQL 2.0's agentic mode allows the LLM to:
- Think strategically — analyze reconnaissance, identify weaknesses, prioritize targets
- Adapt in real-time — if a technique fails, try alternatives automatically
- Chain techniques — combine WAF bypass + payload encoding + exploitation seamlessly
- Operate independently — once started, it continues until objectives are met or scope exhausted
This is essentially giving an AI penetration tester full control, with the tool executing its decisions at machine speed.
- Concurrent Rust execution: Test hundreds of parameters simultaneously
- Multi-URL scanning: Attack entire attack surfaces in minutes, not days
- Crawl mode: Automatically discover and test every form, every parameter, every endpoint
- No human bottleneck: While traditional testing is limited by human analysis time, blackSQL 2.0 tests, adapts, and reports continuously
Beyond detection, blackSQL 2.0 can:
- Enumerate entire databases — schemas, tables, columns, row counts
- Extract sensitive data — user credentials, API keys, PII
- Interactive SQL shell — execute arbitrary SQL queries (read-only by default)
- File operations — read/write files on the database server (when enabled)
- Hash extraction — dump password hashes for offline cracking
Combined with AI guidance, this turns vulnerability detection into systematic data exfiltration.
- WAF-aware payloads — LLM generates obfuscated payloads specifically to bypass detected WAFs
- Context-appropriate testing — adapts to target database type, web framework, and defensive measures
- Intelligent false positive reduction — distinguishes real vulnerabilities from application quirks
In short: This is the first SQLi tool that combines human-level intelligence with machine speed and comprehensive exploitation capabilities. It's a force multiplier for both defenders (finding vulnerabilities before attackers) and attackers (systematic exploitation at scale).
Use only with explicit authorization. This tool is designed for security professionals, not script kiddies.
The traditional approach to SQL injection testing is fundamentally limited: static payloads, manual interpretation, linear thinking. Tools test the same payloads regardless of context, generate machine-only output, and require humans to interpret, decide, and iterate.
blackSQL 2.0 breaks this paradigm by making the tool itself intelligent:
| Challenge | Traditional Tools (incl. current blackSQL) | blackSQL 2.0 Solution |
|---|---|---|
| Intelligence | Static payloads only; no context awareness | 🤖 LLM adapts payloads to target tech, WAF, responses; interprets errors intelligently |
| Reporting | JSON/CSV machine dumps → manual analysis required | 📊 AI generates narrative reports with severity, remediation, and next steps |
| Strategy | Linear testing; human decides each step | 🧠 Agentic mode: LLM autonomously plans and executes testing strategy |
| Performance | Python + GIL → slow, high memory | ⚡ Rust: 10-100× faster, concurrent, single binary |
| Scale | One URL per run; manual iteration | 🌐 Multi-URL, crawl, wordlists, CI/CD integration |
| Exploitation | Basic or unclear scan/exploit separation | 💀 Explicit modes with safety, interactive shell, compliance logging |
Result: The first SQLi tool that thinks, adapts, and reports like a security researcher — but at machine speed and scale.
✅ Error-based: Injects ', ", etc.; detects DB error patterns (MySQL, PostgreSQL, MSSQL, Oracle, SQLite).
✅ Boolean-based: TRUE vs FALSE payloads; compares response differences (similarity threshold 0.95).
✅ Time-based: SLEEP/WAITFOR/pg_sleep; measures delay vs baseline (5s threshold × 0.8).
✅ Union-based: UNION SELECT; infers column count; matches number sequences and DB version strings.
Example: python blacksql.py -u "http://site.com/page?id=1" --level 2 --batch → finds error-based on id.
✅ Static payload lists per technique (31 error, 20 boolean, 18 time, many union).
✅ Scan levels 1–3 (quick: 25% / balanced: 50% / full: 100%).
✅ WAF detection and bypass: comments, encoding, case, whitespace, CHAR().
✅ GET and POST parameters.
✅ Cookies, proxy, timeout (10s), multi-threading (5 threads default).
❌ Single URL per run; no crawl.
❌ No header or JSON body injection; no PUT/PATCH/DELETE.
✅ With --dump: enumerate databases, tables, columns; limited data extraction (via information_schema).
✅ JSON and CSV (timestamped files in output/).
✅ Colorized CLI, progress bar, batch mode.
❌ No Markdown or HTML; no narrative report.
✅ URL validation, parameter/cookie/POST parsing.
❌ No scope/allowlist (can hit out-of-scope hosts).
❌ No dangerous-action confirmation.
❌ No audit log.
✅ 54 unit + integration tests (validator, techniques, real scanner vs fixture server).
✅ Fixture server with error-, boolean-, time-, union-based endpoints.
✅ Manual CLI tests documented (Expected vs Got).
- Scale: Single URL; no multi-URL or crawl.
- Injection points: Only GET/POST; no headers, JSON, or other methods.
- Reports: JSON/CSV only; no human-readable narrative.
- LLM: None; no smart payloads or report generation.
- Safety: No scope enforcement, confirmation, or audit.
- Exploitation: Dump mode unclear; no interactive shell or file output.
The new blackSQL is AI-first. Every component is designed to leverage LLM intelligence while maintaining the proven detection core of the current version.
Single binary — no Python or runtime. Fast, concurrent, low memory. Linux, macOS, Windows.
✅ Same four techniques (error, boolean, time, union).
✅ Same DB fingerprinting and WAF detection/bypass.
✅ Same scan levels 1–3.
➕ Optional: out-of-band (OOB), second-order SQLi, classify blind vs in-band.
Two modes:
- Scan (default): Detect only; no extraction or execution.
- Exploit (opt-in): With
--dumpor--exploit; enumerate and extract.
Exploitation features:
✅ Database, table, column enumeration.
✅ Dump to file (e.g. --dump users -o users.csv).
✅ Interactive shell (read-only SQL after finding; step-by-step commands).
✅ Safe PoC only (no DROP/DELETE/UPDATE unless explicitly enabled).
✅ Audit trail (log every payload and response).
➕ Optional: hash extraction, privilege check, read file (LOAD_FILE/pg_read_file), write file (INTO OUTFILE/COPY) behind flags + confirmation.
Example: blacksql --url "..." --dump users -o users.csv → if vuln found, enumerates and dumps users table safely.
This is what makes blackSQL 2.0 the first of its kind. Optional Ollama integration turns a traditional scanner into an intelligent security researcher.
🧠 Intelligence Capabilities:
✅ Context-Aware Payload Generation — LLM analyzes target (URL structure, tech stack, server responses, WAF patterns) and generates custom payloads specifically crafted for that target. No more blind spraying of generic payloads.
✅ Intelligent Response Interpretation — "Is this a database error or just broken HTML?" The LLM understands context, drastically reducing false positives and identifying subtle vulnerabilities humans might miss.
✅ Narrative Report Generation — Forget JSON dumps. Get a comprehensive Markdown report with:
- Executive summary with severity scoring
- Technical details with affected parameters
- Working payloads and their impact
- Remediation advice tailored to the vulnerability
- Code examples for fixes
✅ Guided Exploitation — After finding a vulnerability, LLM suggests: "This appears to be MySQL. Try UNION SELECT for column enumeration, then query information_schema for tables." Step-by-step, intelligent guidance.
✅ Single-Call Report Mode — Send all findings to LLM once; receive a complete professional security report. Perfect for clients and management.
┌─────────────────────────────────────────────────────────────────────┐
│ 🚀 AGENTIC MODE - AUTONOMOUS AI PENTESTER 🚀 │
│ │
│ ⚡ THE MOST DANGEROUS FEATURE ⚡ │
│ AI takes full control • Thinks • Adapts • Exploits │
└─────────────────────────────────────────────────────────────────────┘
✅ 🚀 Agentic Mode (Autonomous) — The most dangerous feature. LLM autonomously:
- Decides which tests to run based on initial reconnaissance
- Calls tools:
test_payload(),enumerate_database(),check_waf(),report_vulnerability() - Adapts strategy in real-time based on responses
- Chains techniques automatically
- Thinks and acts like a penetration tester, but at machine speed
Example:
# Traditional: static report
blacksql --url "..." --llm-report -o report.md
# Agentic: AI takes control
blacksql --url "..." --agentic --max-depth 3
# LLM autonomously discovers, tests, and exploits vulnerabilities✅ Single URL (like current).
✅ Multi-URL: --url-file urls.txt or stdin → scan many in one run.
✅ Crawl mode: start from one URL; discover links/forms; inject all params.
✅ Injection points: GET, POST, headers (X-Forwarded-For, User-Agent, Referer, Cookie), JSON body, and PUT/PATCH/DELETE for APIs.
➕ Optional: OOB, second-order, classify blind vs in-band.
Example: blacksql --url-file targets.txt --level 2 → scans 100 URLs from a file with balanced payloads.
✅ Built-in payload sets (same as current).
✅ Payload from file: --payload-file payloads.txt.
✅ Templates: placeholders like {PARAM}, {DB}, {TABLE} filled by tool or LLM.
✅ DB-specific mode: --db mysql → only MySQL payloads (faster, fewer false positives).
✅ Prioritization: try high-probability payloads first (quote, 1=1).
✅ More WAF bypass: polyglots, encoding chains, comment/case variants.
✅ Formats: JSON, CSV, Markdown, HTML, SARIF (GitHub CodeQL, IDEs, CI).
✅ Evidence: store request/response pairs per finding.
✅ CI: exit codes (0 clean, 1 error, 2 vuln); --fail-on critical.
✅ Config file: YAML/TOML for defaults (URL, payloads, headers, proxy, timeouts).
Example: blacksql --url "..." --format markdown -o report.md → full Markdown report (LLM or template).
blackSQL allows testing on any site (like any Kali Linux tool) but includes optional helpers for responsible use:
✅ Scope/allowlist (optional): --scope "*.example.com" restricts scanning to in-scope hosts; reject others. Prevents accidental out-of-scope testing. Not enforced by default — user can scan any site they are authorized to test.
✅ Rate limiting (optional): cap requests/sec or add delay to avoid overloading targets.
✅ Confirmation (optional): prompt before destructive actions (dump, exploit, write-file).
✅ Audit log (optional): immutable log for compliance and accountability.
Disclaimer: Use only on systems you own or have explicit permission to test. Unauthorized access is illegal.
Example: blacksql --url "..." --scope "*.example.com" --rate-limit 10 → scan only example.com subdomains, max 10 req/sec.
✅ Resume: save progress; resume interrupted scan.
✅ Dry-run: list what would be tested without sending requests.
✅ Verbosity: -q, default, -v, -vv (payloads + responses).
➕ Optional: plugins/scripts (e.g. Lua) for custom payloads or parsing.
| Area | Current (Python) | New (Rust) | Improvement |
|---|---|---|---|
| Binary | Python script + pip deps | Single Rust binary | No runtime; faster distribution |
| Detection | 4 techniques, WAF bypass, DB detection | Same + optional OOB/second-order, blind vs in-band | More coverage |
| Exploitation | --dump (enum + limited extract); no clear scan/exploit split |
Explicit scan vs exploit; dump to file; interactive shell; safe PoC; audit | Safer, more capable |
| LLM | None | Optional Ollama: payloads, interpretation, Markdown report, guided, agentic | Smarter, human-readable |
| Targets | Single URL | + Multi-URL/wordlist + crawl | Scale to many |
| HTTP | GET, POST, cookies, proxy | + Headers, JSON body, PUT/PATCH/DELETE | API-ready |
| Payloads | Static + WAF bypass, levels 1–3 | + Payload file, templates, DB-specific, prioritization | Customizable |
| Output | JSON, CSV, console | + Markdown, HTML, SARIF; evidence; CI; config | CI-friendly, readable |
| Safety | Validation, batch | + Scope, rate limit, confirmation, audit | Responsible pentesting |
| UX | Progress, colors, threads | + Resume, dry-run, verbosity, optional plugins | Better DX |
| Feature | Current | New | Notes |
|---|---|---|---|
| Runtime | Python + pip | Rust binary | Faster, no runtime |
| Error-based | ✅ | ✅ | Kept |
| Boolean-based | ✅ | ✅ | Kept |
| Time-based | ✅ | ✅ | Kept |
| Union-based | ✅ | ✅ | Kept |
| DB fingerprinting | ✅ | ✅ | Kept |
| WAF detection | ✅ | ✅ | Kept |
| WAF bypass | ✅ | ✅ + more | Polyglots, chains |
| Scan levels | ✅ 1–3 | ✅ 1–3 | Kept |
| OOB / second-order | ❌ | ➕ Optional | New |
| Blind vs in-band | ❌ | ➕ Optional | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| Mode separation | ❌ | ✅ Scan (default) vs Exploit (opt-in) | Clear intent |
| DB/table/column enum | ✅ --dump |
✅ | Kept |
| Data extraction | ✅ Limited | ✅ Controlled (row limit) | Improved |
| Dump to file | ❌ | ✅ CSV, etc. | New |
| Interactive shell | ❌ | ✅ Read-only by default | New |
| Safe PoC only | ✅ Yes (no DROP/DELETE/UPDATE unless explicit) | New | |
| Audit trail | ❌ | ✅ Log payloads + responses | New |
| Hash extraction | ❌ | ➕ Optional | New |
| Privilege check | ❌ | ➕ Optional | New |
| Read/write file | ❌ | ➕ Optional (behind flags) | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| LLM integration | ❌ | ✅ Optional Ollama | New |
| Payload suggestion | ❌ | ✅ Context-aware | New |
| Response interpretation | ❌ | ✅ Fewer false positives | New |
| Markdown report | ❌ | ✅ Summary, severity, remediation | New |
| Guided next steps | ❌ | ✅ After finding | New |
| Single-call mode | ❌ | ✅ One report from findings | New |
| Agentic mode | ❌ | ✅ LLM calls tools; adaptive | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| Single URL | ✅ | ✅ | Kept |
| Multi-URL / wordlist | ❌ | ✅ --url-file or stdin |
New |
| Crawl mode | ❌ | ✅ Discover links/forms | New |
| GET/POST | ✅ | ✅ | Kept |
| Headers as injection | ❌ | ✅ X-Forwarded-For, User-Agent, etc. | New |
| JSON body | ❌ | ✅ Content-Type: application/json | New |
| PUT/PATCH/DELETE | ❌ | ✅ API testing | New |
| OOB | ❌ | ➕ Optional | New |
| Second-order | ❌ | ➕ Optional | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| Built-in payload sets | ✅ | ✅ | Kept |
| Levels 1–3 | ✅ | ✅ | Kept |
| WAF bypass | ✅ | ✅ + more | Polyglots, chains |
| Payload from file | ❌ | ✅ --payload-file |
New |
| Templates | ❌ | ✅ {PARAM}, {DB}, {TABLE} |
New |
| DB-specific | ❌ | ✅ --db mysql |
New |
| Prioritization | ❌ | ✅ High-probability first | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| JSON | ✅ | ✅ | Kept |
| CSV | ✅ | ✅ | Kept |
| Markdown | ❌ | ✅ | New |
| HTML | ❌ | ✅ | New |
| SARIF | ❌ | ✅ CodeQL/CI | New |
| Evidence | ❌ | ✅ Request/response pairs | New |
| CI exit codes | ❌ | ✅ 0/1/2, --fail-on |
New |
| Config file | ❌ | ✅ YAML/TOML | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| URL validation | ✅ | ✅ | Kept |
| Scope/allowlist | ❌ | ✅ Only scan in-scope | New |
| Rate limiting | ❌ | ✅ Req/sec cap | New |
| Dangerous confirm | ❌ | ✅ --yes or prompt |
New |
| Audit log | ❌ | ✅ Optional | New |
| Feature | Current | New | Notes |
|---|---|---|---|
| Progress bar | ✅ | ✅ | Kept |
| Colors | ✅ | ✅ | Kept |
| Batch mode | ✅ | ✅ | Kept |
| Resume | ❌ | ✅ | New |
| Dry-run | ❌ | ✅ | New |
| Verbosity | ✅ -q / -v / -vv |
Improved | |
| Plugins | ❌ | ➕ Optional (e.g. Lua) | New |
# Scan one URL, level 2, batch mode
python blacksql.py -u "http://site.com/page?id=1" --level 2 --batch
# Output: JSON + CSV in output/; console shows findingsLimitation: Can only scan one URL; to scan 100 URLs you'd run 100 times.
# Scan many URLs from file, level 2, Markdown report
blacksql --url-file targets.txt --level 2 --format markdown -o report.md
# Scan + exploit: crawl site, enumerate on any finding, dump to file
blacksql --url "http://site.com" --crawl --exploit --dump users -o users.csv --scope "*.site.com"
# LLM-powered report with Ollama
blacksql --url "http://site.com/page?id=1" --llm-report -o report.md
# CI: fail build if critical SQLi found
blacksql --url-file urls.txt --fail-on criticalNew capability: Scan 100 URLs in one run, crawl a site automatically, generate human-readable reports, and enforce scope/safety.
╔════════════════════════════════════════════════════════════╗
║ 🏗️ AI-POWERED ARCHITECTURE 🏗️ ║
║ Rust Core + LLM Brain + Exploitation Engine ║
╚════════════════════════════════════════════════════════════╝
┌────────────────────────────────────────────────────────┐
│ blackSQL (Rust) │
├────────────────────────────────────────────────────────┤
│ 1. Input: URL(s), wordlist, or crawl start │
│ │
│ 2. Scanner: GET/POST/headers/JSON → inject payloads │
│ ├── Error-based, Boolean, Time, Union │
│ ├── WAF detection & bypass │
│ └── DB fingerprinting │
│ │
│ 3. Exploitation (if --exploit / --dump): │
│ ├── Enumerate: DBs → tables → columns │
│ ├── Dump data (row limit, safe PoC) │
│ ├── Interactive shell (opt) │
│ └── Audit trail │
│ │
│ 4. Reporting: │
│ ├── JSON/CSV (machine) │
│ └── Markdown/HTML/SARIF (human/CI) │
│ │
│ 5. LLM (optional, if Ollama configured): │
│ ├── Payload suggestion & response interpretation │
│ ├── Markdown report generation │
│ ├── Guided next steps │
│ └── Agentic mode (tool-calling) │
│ │
│ 6. Safety: scope, rate limit, confirmation, audit │
└────────────────────────────────────────────────────────┘
Same flow as current but with more injection points, optional LLM layer, and explicit safety checks.
Planned implementation order:
- Phase 1 – Rust core: Detection (4 techniques), payloads, WAF bypass, extraction (--dump), JSON/CSV, CLI. Port current to Rust; no new features yet.
- Phase 2 – Exploitation & safety: Explicit scan vs exploit; scope/allowlist; dangerous-action confirmation; dump to file; optional interactive shell (read-only default).
- Phase 3 – LLM: Ollama integration: Markdown report, response interpretation, payload suggestion; then agentic mode with tools.
- Phase 4 – Coverage: Multi-URL/wordlist; config file; crawl; headers/JSON/methods.
- Phase 5 – Reporting & CI: Markdown/HTML, SARIF, exit codes, evidence, rate limiting.
- Phase 6 – Nice-to-have: Payload file/templates, hash extraction, resume, plugins, OOB/second-order.
- Not a full sqlmap replacement — focused on SQLi; no XSS/SSRF/other.
- No unattended abuse — operator-driven; LLM assists, not replaces human.
- No built-in CVE DB — detection is payload/response-based.
- Legal use only — authorized testing only.
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║ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚══════╝ ╚═════╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ║
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║ 🤖 FIRST AI-POWERED SQLI TOOL • NO COMPETITION • REVOLUTIONARY 🤖 ║
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blackSQL 2.0 is the first AI-powered SQL injection tool. No other tool combines LLM intelligence with comprehensive SQLi testing and exploitation capabilities.
| Aspect | Traditional Tools | blackSQL 2.0 | Revolutionary Advantage |
|---|---|---|---|
| Intelligence | ❌ Static only | 🤖 AI/LLM-powered | Context-aware payloads, intelligent error interpretation, agentic testing |
| Language | Python/interpreted | ⚡ Rust binary | 10-100× faster, single file, no runtime |
| Reporting | JSON/CSV dumps | 📊 AI narrative reports | Human-readable Markdown/HTML with remediation advice |
| Strategy | Linear, manual | 🧠 Autonomous agentic mode | LLM plans and executes testing strategy independently |
| Scale | Single URL | 🌐 Multi-URL + crawl | Test hundreds of targets in one run |
| Injection Points | GET/POST | 🎯 Headers + JSON + methods | API-ready, comprehensive coverage |
| Exploitation | Basic or unclear | 💀 Explicit safe modes + shell | Interactive SQL shell, controlled dumping, audit trails |
| CI/CD | Manual only | 🔧 SARIF + exit codes | GitHub CodeQL, automated security gates |
| Testing | Often minimal | ✅ 54 comprehensive tests | Proven detection + integration tests |
- For Security Researchers: An AI assistant that thinks like you, but tests faster and reports better
- For Pentesters: Autonomous intelligent testing that scales to entire attack surfaces
- For Red Teams: Context-aware exploitation with compliance logging and safety guardrails
- For DevSecOps: CI/CD integration with machine-readable SARIF and human-readable reports
This isn't just an improvement — it's a paradigm shift in SQL injection testing.
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🤖 AI • RUST • AUTONOMOUS 🤖
blackSQL 2.0: The First AI-Powered SQLi Tool
Think Like a Pentester. Act Like a Machine.
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⚠️ USE RESPONSIBLY • AUTHORIZED TESTING ONLY • LEGAL CONSEQUENCES FOR ABUSE ⚠️
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This is the design & technical specification for blackSQL 2.0 — the first AI-powered SQL injection tool. Current = Python version (proven, tested). New = Rust + AI rewrite with revolutionary capabilities.