A security intelligence platform that aggregates, classifies, and enriches AI and LLM security data from 45+ sources. Each record includes 48 structured fields with LLM-based classification, EPSS scores, CISA KEV status, CAPEC mappings, and MITRE ATLAS technique IDs. Built by an Information Systems Security researcher to help security teams and developers stay ahead of the AI threat landscape.
2,479
Total Items
45
Data Sources
48
Fields per Item
Oct 2012 -- Mar 2026
Date Coverage
Mar 21, 2026
Last Updated
CC-BY-4.0
License
/api/v1/issuesFilterable, paginated JSON with all enrichment fields/api/issues/exportFull dataset download as comma-separated values/api/v1/issues/stixThreat intelligence format for MISP and OpenCTINo authentication required. 60 requests per minute. CORS enabled. Full API docs
import pandas as pd
# Load full dataset (CSV)
df = pd.read_csv("https://aisecwatch.com/api/issues/export")
# Or use the JSON API with filtering
import requests
resp = requests.get("https://aisecwatch.com/api/v1/issues", params={
"severity": "critical",
"limit": 100,
})
data = resp.json()["data"]No API key required. 60 requests/minute. All data CC-BY-4.0.
36 active data sources monitored every hour, including NVD, CISA KEV, GitHub Advisory, arXiv, and 20+ cybersecurity and AI news feeds. View all sources.
Incoming items are deduplicated by URL and CVE ID to prevent double-counting.
Each item is processed by an LLM using a versioned prompt (currently v3) that assigns severity, issue type, attack type, affected vendors, and a confidence score (0–1).
Vulnerabilities with CVE IDs are enriched with EPSS exploit probability scores (FIRST API), CISA Known Exploited Vulnerabilities catalog status, CWE-to-CAPEC attack pattern mapping, and CVSS vector parsing.
Data is accessible via the web interface, public REST API (60 req/min, CORS enabled), STIX 2.1 threat intelligence feed, webhooks with HMAC-SHA256 signing, RSS feeds by category, CSV export, and weekly email newsletters.
Exploit Prediction Scoring System
30-day exploit probability from FIRST. Scores range from 0 to 1, indicating how likely a vulnerability is to be exploited in the wild within the next 30 days.
Known Exploited Vulnerabilities Catalog
Known exploitation status from CISA, including whether the vulnerability has been observed in active exploitation and whether it is associated with ransomware campaigns.
Common Attack Pattern Enumeration and Classification
Attack pattern taxonomy mapping derived from CWE IDs. Maps weaknesses to known attack patterns to help defenders understand exploitation techniques.
Common Vulnerability Scoring System
Vector string parsing for attack characteristics including attack vector, complexity, privileges required, user interaction, and impact metrics.
Adversarial Threat Landscape for AI Systems
AI/ML-specific attack taxonomy mapping. Attack types identified by the classifier are mapped to ATLAS technique IDs, enabling alignment with enterprise AI risk frameworks.
If you reference AI Sec Watch or use its dataset in academic work, industry reports, or threat intelligence analysis, please use one of the citation formats below. You can copy the citation text or download it as a file for import into your reference manager (Zotero, Mendeley, EndNote, etc.).
Use this when referencing the AI Sec Watch website, its intelligence feed, or its methodology.
@misc{luu2026aisecwatch,
author = {Luu, T.J.},
title = {{AI Sec Watch}: A Security Intelligence Platform for {AI} Systems},
year = {2026},
url = {https://aisecwatch.com},
note = {Accessed: 2026-03-21}
}Use this when using exported data (CSV, JSON, JSONL) from AI Sec Watch in quantitative analysis or machine learning experiments.
@misc{luu2026aisecwatch_dataset,
author = {Luu, T.J.},
title = {{AI Sec Watch Dataset}: Structured {AI} Security Threat Intelligence},
year = {2026},
url = {https://aisecwatch.com/api-docs},
note = {45 fields per issue. Available in CSV, JSON, JSONL. Accessed: 2026-03-21}
}Ph.D. candidate, Information Systems, University of Cincinnati
Research focus: AI security, LLM vulnerabilities, information privacy