Upload any PDF, get a full forensic report in seconds. PQ PDF runs 44 engines: behavioral sandbox (six PDF renderers), YARA, ClamAV, ML anomaly detection, JavaScript AST deobfuscation, XFA analysis, CVE exploit pattern matching, PDF signature forgery detection, and offline threat intel across 6.4M+ indicators. Results include MITRE ATT&CK mapping and an AI forensic report. No account needed. Files deleted immediately.
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Maker
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Hey PH! I built this after getting frustrated with VirusTotal returning "0/72" on PDFs that were clearly malicious — signature scanners only catch known threats.
PQ PDF takes a different approach: six renderers parse the same file simultaneously and flag discrepancies (hidden objects, parser-confusion exploits, shadow object trees). Stack that with YARA, ClamAV, ML anomaly detection, JS AST deobfuscation, and 6.4M+ offline threat indicators — all free, no account, zero retention.
Happy to answer how any of the 44 engines work. What would you want to see added?
Report
This is a serious build, Allan. The six-renderer discrepancy idea stood out to me because it feels like a smarter way to catch parser-confusion attacks than relying only on signatures.
Curious which part was hardest to get right: coordinating the different renderers, reducing false positives, or turning all the engine outputs into a report that users can actually understand?
This is a serious build, Allan. The six-renderer discrepancy idea stood out to me because it feels like a smarter way to catch parser-confusion attacks than relying only on signatures.
Curious which part was hardest to get right: coordinating the different renderers, reducing false positives, or turning all the engine outputs into a report that users can actually understand?