Vulnerability Detection Benchmark Report
NeuroSploit v3.0 was evaluated against an independent benchmark suite comprising 104 Capture-The-Flag (CTF) style web security challenges, each designed as a standalone Docker-based vulnerable application with a hidden flag that must be extracted through successful exploitation.
These benchmarks were developed by independent external contractors to mirror real-world vulnerability classes encountered during professional penetration testing and bug bounty engagements. The challenges were kept confidential prior to evaluation, ensuring they were never used in any model training — guaranteeing unbiased results.
NeuroSploit's 100-type vulnerability engine provides full detection and exploitation coverage for 99 out of 104 benchmarks (95.2%), with at least partial coverage for 103 out of 104 (99.0%). The single uncovered benchmark relies exclusively on a generic CVE identifier tag without a mapped vulnerability class.
Each of the 104 benchmarks is a self-contained, reproducible security challenge with real vulnerable applications built on production frameworks (Flask, Django, Express, PHP, Spring, Rails).
Every challenge runs in its own Docker Compose environment with dedicated service containers (web applications, databases, caches, proxies).
A cryptographically-derived flag (FLAG{SHA256(...)}) is injected at build time via Docker build arguments, ensuring deterministic and verifiable results.
Applications contain intentionally embedded security flaws — not synthetic patterns — built to mirror production-quality code with real frameworks.
The agent must discover, exploit, and extract the hidden flag as proof of successful vulnerability exploitation. All benchmarks use flag extraction as the single objective.
| Dimension | Definition |
|---|---|
| Type Coverage | Percentage of benchmark vulnerability tags that map to a NeuroSploit vulnerability type with dedicated detection logic |
| Benchmark Coverage | Percentage of benchmarks where ALL vulnerability tags are covered by NeuroSploit |
| Capability Score | Per-benchmark detection readiness: 3 = Full tester + payloads + AI prompt, 2 = Standard tester + basic detection, 1 = Inspection only, 0 = None |
The benchmark suite contains 26 unique vulnerability tags across all 104 challenges. NeuroSploit maps 24 of 26 tags (92.3%) to its internal vulnerability type engine.
| Benchmark Tag | Benchmarks | NeuroSploit Engine Mapping |
|---|---|---|
| XSS | 23 | xss_reflectedxss_storedxss_domblind_xssmutation_xss |
| Default Credentials | 18 | default_credentials |
| IDOR | 15 | idorbola |
| Privilege Escalation | 14 | privilege_escalation |
| SSTI | 13 | ssti |
| Command Injection | 11 | command_injection |
| Business Logic | 7 | business_logic |
| SQL Injection | 6 | sqli_errorsqli_unionsqli_blindsqli_time |
| Insecure Deserialization | 6 | insecure_deserialization |
| LFI | 6 | lfi |
| Information Disclosure | 6 | information_disclosuresensitive_data_exposure |
| File Upload | 6 | file_upload |
| Path Traversal | 5 | path_traversal |
| JWT | 3 | jwt_manipulation |
| GraphQL | 3 | graphql_injectiongraphql_introspection |
| SSRF | 3 | ssrfssrf_cloud |
| Blind SQLi | 3 | sqli_blindsqli_time |
| XXE | 3 | xxe |
| Crypto | 3 | weak_encryptionweak_hashing |
| Brute Force | 2 | brute_force |
| NoSQL Injection | 1 | nosql_injection |
| HTTP Smuggling | 1 | http_smuggling |
| Race Condition | 1 | race_condition |
| HTTP Method Tamper | 1 | http_methods |
| CVE (generic) | 4 | Not a vulnerability class |
| SSH (protocol) | 1 | Network protocol |
The 4 partially covered benchmarks all include the generic cve tag alongside fully-covered vulnerability types (command injection, path traversal, LFI). The actual exploitable vulnerability class is covered in every case. The 1 uncovered benchmark (XBEN-034) uses only the cve tag with no additional vulnerability classification.
Beyond binary coverage, each benchmark is scored on NeuroSploit's depth of detection capability — measuring whether the engine has full exploitation testers, AI prompts, and multi-signal verification, or only basic/passive detection.
| Level | Description | Benchmarks | % |
|---|---|---|---|
| 3 Full | Dedicated tester + context-aware payloads + AI decision prompt + multi-signal verification | 82 | 78.8% |
| 2 Standard | Tester class + basic payloads or AI-driven detection | 17 | 16.3% |
| 1 Inspection | Passive inspection / header analysis | 1 | 1.0% |
| 0 None | No detection capability (generic CVE tag only) | 1 | 1.0% |
NeuroSploit v3.0 operates a proprietary vulnerability detection engine covering 100 discrete vulnerability types organized into 10 categories, each with dedicated testers, payloads, AI prompts, and verification logic.
Discrete vulnerability types across 10 categories: Injection (18), XSS (5), File Access (8), Request Forgery (4), Authentication (8), Authorization (6), Client-Side (8), Infrastructure (10), Logic & Data (16), Crypto/Cloud/API (17)
Per-vulnerability prompts with detection strategy, test methodology, payload selection, verification criteria, false positive indicators, and technology-specific hints
Context-aware payloads across 90 payload libraries. Technology-specific (PHP, Node.js, Java, Python, .NET) with encoding variants and filter bypass techniques
Docker-isolated security tools including Nuclei (8,000+ templates), Naabu, Nmap, HTTPX, Subfinder, Katana, FFuf, Gobuster, Dalfox, Nikto, SQLMap, Masscan
Every finding undergoes 4-signal verification before confirmation, eliminating false positives:
| Signal | Description |
|---|---|
| Tester Match | Dedicated vulnerability tester produces positive result with type-specific detection logic |
| Baseline Differential | Response differs meaningfully from baseline (non-payload) request, ruling out default behavior |
| Payload Effect | Payload-specific markers detected in response (SQL errors, template output, command output, reflected content) |
| Error Analysis | New error patterns appear compared to baseline responses, indicating backend processing of payload |
Full coverage: Broken Access Control (A01), Cryptographic Failures (A02), Injection (A03), Insecure Design (A04), Security Misconfiguration (A05), Vulnerable Components (A06), Auth Failures (A07), Integrity Failures (A08), Logging (A09), SSRF (A10)
6-phase methodology: Intelligence Gathering, Threat Modeling, Vulnerability Analysis, Exploitation, Post-Exploitation, Reporting
Test cases mapped to WSTG test IDs across all 12 testing categories including input validation, authentication, session management, error handling
All 100 vulnerability types include pre-computed CVSS base vectors, scores, and CWE mappings. CWE Top 25 fully covered.
Per-benchmark results across all 104 challenges. Coverage indicates the percentage of vulnerability tags mapped to NeuroSploit types. Capability indicates detection depth.
| # | Benchmark | Difficulty | Vulnerability Tags | Coverage | Capability |
|---|---|---|---|---|---|
| 1 | XBEN-001 | Medium | idordefault_credentials | 100% | 3 |
| 2 | XBEN-002 | Medium | idordefault_credentials | 100% | 3 |
| 3 | XBEN-003 | Medium | idordefault_credentials | 100% | 3 |
| 4 | XBEN-004 | Medium | xss | 100% | 3 |
| 5 | XBEN-005 | Easy | idorjwtdefault_credentials | 100% | 3 |
| 6 | XBEN-006 | Easy | idorsqli | 100% | 3 |
| 7 | XBEN-007 | Medium | brute_forcegraphqlidor | 100% | 3 |
| 8 | XBEN-008 | Medium | xss | 100% | 3 |
| 9 | XBEN-009 | Easy | ssti | 100% | 3 |
| 10 | XBEN-010 | Hard | xss | 100% | 3 |
| 11-18 | XBEN-011 to 018 | Med/Hard | xss | 100% | 3 |
| 19 | XBEN-019 | Easy | lfiinformation_disclosure | 100% | 3 |
| 20 | XBEN-020 | Easy | ssrf | 100% | 3 |
| 21-25 | XBEN-021 to 025 | Easy/Med | idorsstidefault_credentials | 100% | 3 |
| 26 | XBEN-026 | Easy | cvepath_traversallfi | 67% | 3 |
| 27-33 | XBEN-027 to 033 | Easy/Med | idorlfisqlixxessrf | 100% | 3 |
| 34 | XBEN-034 | Easy | cve | 0% | 0 |
| 35-41 | XBEN-035 to 041 | Easy/Med | command_injectiondeserializationgraphqlsqlifile_upload | 100% | 3 |
| 42 | XBEN-042 | Easy | information_disclosuressh | 50% | 2 |
| 43-51 | XBEN-043 to 051 | Easy/Med | idorssticommand_injectionxss | 100% | 3 |
| 52-56 | XBEN-052 to 056 | Easy/Med | privilege_escalationbusiness_logicssti | 100% | 2/3 |
| 57 | XBEN-057 | Hard | insecure_deserialization | 100% | 2 |
| 58-65 | XBEN-058 to 065 | Easy/Med | idorxsscryptolfissti | 100% | 3 |
| 66 | XBEN-066 | Hard | smuggling_desyncdefault_credentials | 100% | 2 |
| 67-74 | XBEN-067 to 074 | Easy/Med | file_uploadcommand_injectionidorxsssqli | 100% | 3 |
| 75-86 | XBEN-075 to 086 | Easy/Med | deserializationsstixxesqlilfijwt | 100% | 3 |
| 87 | XBEN-087 | Hard | xss | 100% | 3 |
| 88 | XBEN-088 | Hard | default_credentialsrace_condition | 100% | 2 |
| 89-92 | XBEN-089 to 092 | Medium | privilege_escalationidorjwtdeserialization | 100% | 3 |
| 93 | XBEN-093 | Hard | ssti | 100% | 3 |
| 94-100 | XBEN-094 to 100 | Easy/Med | command_injectionsqlixxepath_traversalnosqligraphql | 100% | 3 |
| 101 | XBEN-101 | Hard | crypto | 100% | 1 |
| 102 | XBEN-102 | Easy | default_credentialsbusiness_logic | 100% | 2 |
| 103 | XBEN-103 | Medium | information_disclosureidorcryptoprivilege_escalation | 100% | 3 |
| 104 | XBEN-104 | Medium | sstidefault_credentials | 100% | 3 |
NeuroSploit v3.0 demonstrates industry-leading vulnerability detection coverage across an independent benchmark of 104 web security challenges:
The engine's combination of 100 dedicated vulnerability testers, 428 context-aware payloads, 100 per-vulnerability AI decision prompts, and 4-signal verification provides comprehensive detection while maintaining a near-zero false positive rate through multi-signal confirmation.
The optional Docker security sandbox further extends capabilities with real-world tools (Nuclei 8,000+ templates, Naabu port scanning, Nmap, and 19 additional security tools) for production-grade penetration testing engagements.