Purpose-built for document formats: our Document Malware Analysis Server automates deep in-memory stream dissection across Microsoft Office (OLE2, OOXML, RTF, VBA stomping, XLM macros, encrypted VelvetSweatshop containers), PDF filter chains, OneNote, and MIME emails—combining the QuickSand maldoc unpacking engine with real-time AI/RAG prompt-injection security.
Unlike generic file-hash or PE binary sandboxes, the Malware Tracker Document Malware Analysis Server is purpose-built for complex document containers—Built on the memory-safe QuickSand parser framework and extended with cloud-native AI/RAG security rules.
Natively unpacks OLE2, OpenXML, RTF, OneNote, MSO/ActiveMime, compressed VBA macros, p-code stomping, Excel 4.0 XLM formula tokens, and MS-OFFCRYPTO AES/RC4 encrypted lures. Detects embedded PE/ELF/Mach-O payloads obscured via byte transposition, reversed streams, Base64/Hex encoding, and LNK/HTA scriptlets.
Walks obfuscated PDF object trees and /ObjStm streams, recursively decodes filter chains (/FlateDecode with PNG predictors, /ASCII85, /ASCIIHex, /LZW, /RunLength), extracts Widget Base64 payloads, and reconstructs fragmented Tj/TJ text operators.
Pairs recursive QuickSand stream extraction with 252 maldoc exploit, payload, and AI safety YARA rules—blocking invisible PDF rendering (3 Tr), zero-point fonts, OOXML <w:vanish/> hidden prompts, Markdown exfiltration beacons, and spoofed MCP <tool_call> tags in <50ms.
Embed the Document Malware Analysis Server directly into web application upload pipelines, S3/GCS cloud storage buckets, SOAR phishing playbooks, and LLM RAG document ingestion guardrails.
/v1/scan)Inspect user-submitted resumes, invoices, tax forms, and loan documents in <100ms via REST API before writing to disk. Default ?store=false zero-retention execution satisfies strict SOC 2 and HIPAA controls.
Replace 4 GB ClamAV containers with a lightweight serverless function triggered on object creation. Scans incoming files in ~40ms and routes clean documents or isolates weaponized uploads into quarantine buckets.
Terraform Templates →Run the open-source MIT quicksand engine locally and pull our commercial YARA signature feed (GET /v1/rules/pro) covering 67+ Office/PDF CVEs (CVE-2025-24071, CVE-2025-8088), obfuscated executables, and AI safety rules.
/v1/triage)Pre-filter 100% of phishing email attachments in Tines, Shuffle, n8n, or Cortex XSOAR in milliseconds. Returns a deterministic 0–3 risk rating so you only detonate genuinely suspicious files in expensive VMs.
Detects hidden LLM instruction overrides inside PDFs and Word documents—including PDF invisible rendering mode (3 Tr), micro/zero-point fonts, OOXML <w:vanish/> text, spoofed <tool_call> tags, and Markdown exfiltration links.
Traditional signature AV fails on encrypted Office lures, nested PDF filter chains, VBA stomping, and AI prompt injections while consuming 2–4 GB RAM. Read our stream-level detection and latency comparison.
Read Benchmark →Single-request HTTP inspection with structured JSON verdicts, MITRE ATT&CK mappings, and stream offsets.
# Synchronous in-memory scan with zero file retention (?store=false)
curl -X POST "https://api.malwaretracker.com/v1/scan?store=false" \
-H "X-API-Key: mt_live_YOUR_API_KEY" \
-F "file=@attachment.pdf"
# Response (38ms):
{
"verdict": "malicious",
"rating": 3,
"score": 29,
"scan_time_ms": 38.4,
"zero_retention": true,
"threat_intel": {
"cves": ["CVE-2025-8088"],
"mitre_attack": ["T1059", "T1203"],
"streams_analyzed": 14
},
"ai_safety": {
"safe": false,
"prompt_injection_detected": true,
"findings": [
{
"rule": "ai_pdf_invisible_or_zero_font_text",
"description": "AI/RAG evasion: PDF stream uses invisible text rendering mode (3 Tr)"
}
]
}
}
// Next.js / Express Upload Verification
export async function verifyUploadedDocument(fileBuffer, filename) {
const form = new FormData();
form.append("file", new Blob([fileBuffer]), filename);
const res = await fetch(
"https://api.malwaretracker.com/v1/scan?store=false",
{
method: "POST",
headers: { "X-API-Key": process.env.MALWARETRACKER_API_KEY },
body: form,
}
);
const report = await res.json();
if (report.verdict !== "clean" || !report.ai_safety.safe) {
throw new Error(`Upload rejected: ${report.verdict} (${report.threat_intel.cves.join(", ")})`);
}
return report;
}
import requests
def scan_document_before_ingestion(file_path: str, api_key: str) -> dict:
with open(file_path, "rb") as f:
resp = requests.post(
"https://api.malwaretracker.com/v1/scan",
headers={"X-API-Key": api_key},
params={"store": "false"},
files={"file": f},
timeout=15,
)
resp.raise_for_status()
data = resp.json()
if data["verdict"] != "clean" or not data["ai_safety"]["safe"]:
raise PermissionError(f"Blocked untrusted document: {data['verdict']}")
return data
# Tier-0 Static Triage for Tines, Shuffle, n8n, or Cortex XSOAR
curl -X POST "https://api.malwaretracker.com/v1/triage" \
-H "X-API-Key: mt_live_YOUR_API_KEY" \
-F "file=@reported_phish.doc"
# Routing Verdict:
{
"verdict": "malicious",
"rating": 3,
"escalate_to_sandbox": true,
"recommended_action": "quarantine_and_detonate",
"ai_prompt_injection": false,
"cves": ["CVE-2025-24071"],
"mitre_attack": ["T1187", "T1203"],
"scan_time_ms": 24.1
}
# Download commercial Pro Exploit CVE & AI Safety YARA bundle curl -sSL "https://api.malwaretracker.com/v1/rules/pro" \ -H "X-API-Key: mt_live_YOUR_PRO_KEY" \ -o quicksand_pro_rules.yara # Run locally with open-source QuickSand CLI: quicksand -e quicksand_pro_rules.yara suspicious.doc
Instant self-serve provisioning backed by Stripe. No enterprise sales calls required.
For security researchers, evaluation, and low-volume apps.
POST /v1/scan (<100ms API)?store=false modeFor applications scanning user-uploaded PDFs and Office docs.
/v1/triage)High-volume Analysis Server API + Pro YARA feed + Bucket Guard.
/v1/rules/pro)Annual commercial YARA signature feed for internal pipelines.
/v1/rules/pro)