Vaibhav A. Diwadkar

14 papers B 1Journal 9Unranked 4
YearRankTypeTitle / Venue / Authors
2024 J jnl
Neuroinformatics
Michael J. Catanzaro, Sam Rizzo, John J. Kopchick, Asadur Chowdury, David R. Rosenberg, Peter Bubenik, Vaibhav A. Diwadkar
2019 conf
ICIAP (2)
A. Yamin, Michael Dayan, Letizia Squarcina, Paolo Brambilla, Vittorio Murino, Vaibhav A. Diwadkar, Diego Sona
2019 conf
ISBI
Abubakar Yamin, Michael Dayan, Letizia Squarcina, Paolo Brambilla, Vittorio Murino, Vaibhav A. Diwadkar, Diego Sona
2019 J jnl
CoRR
A. Yamin, Michael Dayan, Letizia Squarcina, Paolo Brambilla, Vittorio Murino, Vaibhav A. Diwadkar, Diego Sona
2019 conf
BHI
Muhammad Abubakar Yamin, Michael Dayan, Letizia Squarcina, Vaibhav A. Diwadkar, Paolo Brambilla, Vittorio Murino, Diego Sona
2019 J jnl
NeuroImage
Eric A. Woodcock, Mark K. Greenwald, Dalal Khatib, Vaibhav A. Diwadkar, Jeffrey A. Stanley
2018 J jnl
NeuroImage
Otto Muzik, Kaice T. Reilly, Vaibhav A. Diwadkar
2017 J jnl
NeuroImage
Jeffrey A. Stanley, Ashley Burgess, Dalal Khatib, Karthik Ramaseshan, Muzamil Arshad, Helen Wu, Vaibhav A. Diwadkar
2012 J jnl
NeuroImage
Erik C. Brown, Otto Muzik, Robert Rothermel, Naoyuki Matsuzaki, Csaba Juhász, Aashit Shah, Marie D. Atkinson, Darren Fuerst, Sandeep Mittal, Sandeep Sood, Vaibhav A. Diwadkar, Eishi Asano
2011 J jnl
NeuroImage
Jeong-Won Jeong, Vaibhav A. Diwadkar, Carla D. Chugani, Piti Sinsoongsud, Otto Muzik, Michael E. Behen, Harry T. Chugani, Diane C. Chugani
2011 J jnl
NeuroImage
Vaibhav A. Diwadkar, Patrick Pruitt, Dhruman Goradia, Eric Murphy, Neil Bakshi, Matcheri S. Keshavan, Usha Rajan, Andrew Reid, Caroline Zajac-Benitez
2011 J jnl
NeuroImage
Mihály Bányai, Vaibhav A. Diwadkar, Péter Érdi
2011 B conf
IJCNN
Péter Érdi, Mihály Bányai, Vaibhav A. Diwadkar, Balázs Ujfalussy
2008 conf
ICANN (2)
Péter Érdi, Vaibhav A. Diwadkar, Balázs Ujfalussy
redb/extractors/js_extractors/scripts/js-xray-runner.js
← Index redb/extractors/js_extractors/scripts/js-xray-runner.js javascript
#!/usr/bin/env node
// Bridge between the Python JS pipeline and @nodesecure/js-x-ray.
//
// Usage: node js-xray-runner.js <path-to-js-file>
//   stdout  one JSON object: {"obfuscator": <name|null>, "warnings": [...]}
//   stderr  human-readable error on failure
//   exit 0  analysis ran (the file may still be benign — see "obfuscator")
//   exit 1  the file could not be read or analysed
//
// Each warning is emitted as {kind, value} so the Python side can tag
// supporting signals (encoded-literal, short-identifiers, suspicious-literal,
// unsafe-stmt) without having to mirror js-x-ray's whole schema.
//
// js-x-ray ≥7 ships as an ES module, which CommonJS `require()` cannot load
// from a `.js` script — the dynamic `import()` below is what makes the
// bridge work without renaming the file to `.mjs` or adding `"type":
// "module"` to package.json (which would break tools that still
// `require()` from this directory).

const fs = require("fs");
const path = require("path");

function fail(msg) {
  process.stderr.write(msg + "\n");
  process.exit(1);
}

async function main() {
  const target = process.argv[2];
  if (!target) fail("usage: js-xray-runner.js <file>");

  let source;
  try {
    source = fs.readFileSync(target, "utf8");
  } catch (e) {
    fail(`read failed: ${e.message}`);
  }

  // The legacy `runASTAnalysis` function is deprecated (removed in v8); the
  // current API is the `AstAnalyser` class. Both produce a result with the
  // same `warnings` shape, so the rest of the bridge is unchanged.
  let AstAnalyser;
  try {
    ({ AstAnalyser } = await import("@nodesecure/js-x-ray"));
  } catch (e) {
    fail(`@nodesecure/js-x-ray not installed (run \`npm install\` in ${path.dirname(__filename)}): ${e.message}`);
  }

  // js-x-ray defaults to module-mode parsing, which rejects scripts that
  // (legally) use reserved words as identifiers, top-level `return`, etc.
  // A lot of real-world JS malware is script-style (WScript/HTA bodies,
  // pasted snippets) — retrying in script mode catches those without
  // pulling in a more lenient parser. Both attempts share the same
  // analyser; only the parse mode flips. If both fail, the original error
  // (module-mode) is reported because that's the more informative one for
  // genuinely broken sources.
  let result;
  const analyser = new AstAnalyser();
  let firstErr;
  try {
    result = await analyser.analyse(source, { module: true });
  } catch (e) {
    firstErr = e;
    try {
      result = await analyser.analyse(source, { module: false });
    } catch (e2) {
      fail(`js-x-ray analysis failed: ${firstErr.message}`);
    }
  }

  const warnings = (result.warnings || []).map((w) => ({
    kind: w.kind,
    value: w.value !== undefined ? w.value : null,
  }));

  // js-x-ray flags the obfuscator family in a warning whose kind is
  // "obfuscated-code" and whose value names the family (jsfuck, obfuscator.io,
  // freejsobfuscator, morse, jjencode, ...). Absent => not detected.
  const obfWarning = warnings.find((w) => w.kind === "obfuscated-code");
  const obfuscator = obfWarning ? obfWarning.value : null;

  // js-x-ray runs its own AST internally with a modern parser, so its
  // identifier-length average is the only path the Python pipeline has to
  // that signal on ES2015+ sources — pyjsparser is ES5.1-only and silently
  // drops to 0 the moment it hits destructuring, classes, optional chaining,
  // etc. Surfacing this lets the heuristic's `avg_identifier_length<2`
  // strong signal fire on real obfuscator.io output. `null` when the value
  // is missing or non-numeric (defensive — older js-x-ray builds may differ).
  const idsLengthAvg =
    typeof result.idsLengthAvg === "number" && !Number.isNaN(result.idsLengthAvg)
      ? result.idsLengthAvg
      : null;

  process.stdout.write(JSON.stringify({ obfuscator, warnings, idsLengthAvg }));
}

main().catch((e) => fail(e.message || String(e)));