MALICIOUS
118
Risk Score
Malware Insights
MITRE ATT&CK
T1059.003 JavaScript
T1559.002 Component Object Model
The ML classifier strongly indicates maliciousness, supported by heuristics for JavaScript actions and ASCIIHexDecode filters, which are commonly used in PDF exploits. The presence of JavaScript suggests an attempt to execute malicious code, likely to download a second-stage payload or exploit a vulnerability within the PDF reader.
Machine Learning
- Nyx PDF Classifier malicious score 0.9998
Heuristics 3
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JavaScript action low 1 related finding PDF_JAVASCRIPTPDF contains a /JavaScript action. Generic JavaScript is common in benign forms; specific dangerous APIs are scored by separate rules.
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PDF JavaScript rebuilds a builtin via replace() to run a char-code array critical PDF_JS_REPLACE_OBFUSCATED_CHARCODE_BUILDERDecoded PDF JavaScript resolves a String builtin from a junked literal — e.g. String['eQvoaol3'.replace(/[3oQS5]/g,'')] yielding fromCharCode/eval — and feeds a large numeric char-code array through it to rebuild and execute the next stage. Dynamically reconstructing a builtin name by stripping junk characters has no benign purpose; paired with the char-code payload array it is an unambiguous obfuscated-JavaScript exploit dropper.
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ASCIIHexDecode filter (with exploit indicators) medium PDF_FILTER_HEXHex-encoding filter present alongside exploit delivery indicators — often used to hide payload or shellcode bytes
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