Generic LLMs hallucinate vulnerabilities. The RAG Security Knowledge Engine grounds every AI finding in 80,000+ real-world security documents — Exploit-DB, CWE, CVE, OWASP, Nuclei templates, and your own scan history. Zero guesswork.
LLMs trained on general data lack the depth to reliably assess real-world vulnerabilities. RAG closes that gap with grounded retrieval from curated security knowledge.
Standard AI models hallucinate vulnerability names, invent CVE numbers, and generate plausible-sounding but incorrect remediation advice. Confidence scores are unanchored. No memory of what has been seen before or what exploits actually exist in the wild.
Every finding is backed by real Exploit-DB entries, CWE definitions, CVE records, and Nuclei templates retrieved at inference time. Confidence is calibrated against known exploitability data. Findings link back to primary sources.
Web scanner finds SSRF. Code scanner finds unsafe deserialization. Network scanner finds open internal ports. Each tool reports in isolation — the critical attack chain connecting them goes undetected.
The RAG engine ingests context from every scan surface — web traffic, source code, and network topology. 10 severity escalation rules automatically detect attack chains that span multiple tools and promote findings accordingly.
Four-stage pipeline: from raw AI detection to knowledge-verified, severity-calibrated findings with primary source references.
LLM identifies a potential vulnerability in web traffic, source code, or network scan output.
ChromaDB vector search queries 80K+ docs for CWE definitions, known exploits, CVEs, and prior findings matching the detection.
Retrieved context refines severity, adds exploit references, generates PoC suggestions, and calibrates confidence against real-world data.
Verified findings boost related KB documents and are re-ingested, making the engine smarter with every confirmed vulnerability.
Every document is chunked, embedded, and indexed in ChromaDB with HNSW for sub-millisecond retrieval. Source priority and hit counts influence ranking.
Real exploit source code from the world's largest public exploit archive. Mapped to CVEs with proof-of-concept code.
46,000+ exploitsMITRE Common Weakness Enumeration definitions with detection guidance, examples, and remediation patterns.
25 weakness typesNational Vulnerability Database entries with CVSS scores, affected products, and vendor advisories via NVD API v2.0.
CVE recordsProjectDiscovery Nuclei detection templates with matchers, extractors, and severity classifications.
TemplatesComplete OWASP Top 10 categories with attack vectors, security weakness descriptions, and prevention cheat sheets.
10 categoriesCurated payload collections for XSS, SQLi, LFI, SSTI, XXE, command injection, and 60+ other attack categories.
623+ docsDaniel Miessler's SecLists payloads organized by attack type: XSS, SQLi, LFI, CMDi, SSTI, and XXE.
Payload setsNmap vulnerability detection scripts with service fingerprinting and version-based vulnerability checks.
231 scriptsSn1per loot, SILENTCHAIN findings, and SOURCE reports are continuously ingested. Your data trains your engine.
ContinuousWhen AI detects a potential vulnerability, the RAG engine retrieves relevant knowledge in real time to verify, enrich, and calibrate the finding.
The RAG engine ingests context from web traffic (Enterprise), source code (SOURCE), and network topology (Sn1per). When findings from multiple products converge on the same target, 10 severity escalation rules automatically detect compound attack chains.
Every verified finding feeds back into the knowledge base. The more you scan, the better your engine gets.
AI analyzes traffic, code, or network data. RAG retrieves relevant knowledge to enrich each finding with real-world context and severity calibration.
Confirmed findings (Certain/Firm confidence) automatically boost the hit count on related KB documents, promoting them in future retrieval rankings.
Verified findings are re-ingested as new KB documents. Your confirmed vulnerabilities become training data for detecting the same class of issue next time.
The RAG engine is the shared knowledge layer under all four products. One knowledge base. One feedback loop. Four attack surfaces.
Async RAGClient for web app scanning. Surface context ingestion + finding correlation.
DASTCode context ingestion. File paths, sinks, and data flows feed the correlation engine.
SASTJython HTTP bridge to RAG API. Traffic context + finding correlation from Burp Suite.
Burp ExtensionNetwork context ingestion. Ports, services, and technologies enrich the correlation graph.
NetworkFastAPI backend with OpenAPI docs. Query the knowledge base, ingest custom data, submit feedback, and run cross-product correlations programmatically.
The RAG Security Knowledge Engine is included with SILENTCHAIN Enterprise and SOURCE. Start scanning with 80,000+ documents backing every finding.