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lib/injection-model.js

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  1. // Model-backed prompt-injection scoring.
  2. // ---------------------------------------------------------------------------
  3. // The artifact (injection-model.json) is trained + exported by
  4. // merchant-trust-data/models/prompt_injection/train_detector.py — hashed TF-IDF
  5. // (md5 feature hashing, uni+bi-grams) + L2-normalised linear classifier trained
  6. // on TensorTrust attacks vs defenses. The implementation here must stay in
  7. // lockstep with that file; test/injection-model.test.js asserts score parity
  8. // against vectors generated in Python at training time.
  9. //
  10. // Scoring definition (identical on both sides):
  11. // tokens: lowercase, regex [a-z0-9']+
  12. // grams : unigrams + bigrams (joined with one space)
  13. // hash : md5(gram utf-8); idx = int(h[0..8],16) % n_features
  14. // sign = +1 if int(h[8..16],16) is even else -1
  15. // vec : sign-weighted gram counts restricted to exported features,
  16. // x idx, L2-normalised; logit = w·x + intercept; score = sigmoid(logit)
  17. // ---------------------------------------------------------------------------
  19. import { createHash } from 'node:crypto';
  20. import { readFileSync } from 'node:fs';
  22. const TOKEN_RE = /[a-z0-9']+/g;
  24. function loadModel() {
  25. const candidates = [
  26. new URL('./injection-model.json', import.meta.url),
  27. new URL('../../merchant-trust-data/models/prompt_injection/injection-model-v1.json', import.meta.url),
  28. ];
  29. for (const url of candidates) {
  30. try {
  31. const m = JSON.parse(readFileSync(url, 'utf8'));
  32. if (m && m.schema === 'openclaw.injection-model/1' && m.weights && typeof m.threshold === 'number') return m;
  33. } catch { /* try next candidate */ }
  34. }
  35. return null;
  36. }
  38. let cached = loadModel(); // eager: artifact parse (~100ms) must never sit in the decision path
  39. export function getInjectionModel() {
  40. return cached; // null when artifact is absent -> engine skips the model layer
  41. }
  43. function gramsOf(tokens) {
  44. const grams = [];
  45. for (let i = 0; i < tokens.length; i++) {
  46. grams.push(tokens[i]);
  47. if (i + 1 < tokens.length) grams.push(`${tokens[i]} ${tokens[i + 1]}`);
  48. }
  49. return grams;
  50. }
  52. function windowLogit(counts, m) {
  53. // restrict to exported features, apply idf, L2-normalise, dot with weights
  54. const active = []; // [weight, x]
  55. let sq = 0;
  56. for (const [idx, v] of counts) {
  57. const pair = m.weights[String(idx)];
  58. if (!pair) continue;
  59. const x = v * pair[0]; // count * idf
  60. active.push([pair[1], x]);
  61. sq += x * x;
  62. }
  63. const norm = Math.sqrt(sq);
  64. let logit = m.intercept;
  65. if (norm > 0) {
  66. for (const [w, x] of active) logit += w * (x / norm);
  67. }
  68. return { logit: Math.max(-60, Math.min(60, logit)), norm };
  69. }
  71. /** Score one string; returns null when no model artifact is deployed.
  72. *
  73. * Scoring mode comes from the artifact (`scoring.mode`, default `window_max`):
  74. * the text is scored whole AND over sliding token windows (attacks embedded in
  75. * otherwise-benign product text would otherwise be diluted below threshold);
  76. * the final score is the maximum.
  77. */
  78. export function scoreInjectionText(text) {
  79. const m = getInjectionModel();
  80. if (!m) return null;
  81. const tokens = String(text).toLowerCase().match(TOKEN_RE) || [];
  82. if (!tokens.length) return { score: 0, logit: m.intercept, topGrams: [], tokens: 0 };
  84. const win = m.scoring?.mode === 'full_text'
  85. ? { window_tokens: 0, stride_tokens: 0 }
  86. : { window_tokens: m.scoring?.window_tokens ?? 40, stride_tokens: m.scoring?.stride_tokens ?? 20 };
  88. // build evaluation spans: whole text + sliding windows
  89. const spans = [[0, tokens.length]];
  90. if (win.window_tokens > 0 && tokens.length > win.window_tokens) {
  91. for (let start = 0; start + win.window_tokens <= tokens.length; start += win.stride_tokens) {
  92. spans.push([start, start + win.window_tokens]);
  93. }
  94. const tail = tokens.length - win.stride_tokens;
  95. const last = spans[spans.length - 1];
  96. if (tail > 0 && (last[0] !== tail)) spans.push([tail, tokens.length]);
  97. }
  99. let best = null;
  100. for (const [a, b] of spans) {
  101. const slice = tokens.slice(a, b);
  102. const counts = new Map();
  103. const gramMeta = new Map();
  104. for (const g of gramsOf(slice)) {
  105. const h = createHash('md5').update(g, 'utf8').digest('hex');
  106. const idx = parseInt(h.slice(0, 8), 16) % m.n_features;
  107. const sign = parseInt(h.slice(8, 16), 16) % 2 === 0 ? 1 : -1;
  108. counts.set(idx, (counts.get(idx) || 0) + sign);
  109. if (!gramMeta.has(g)) gramMeta.set(g, { idx, sign });
  110. }
  111. const { logit, norm } = windowLogit(counts, m);
  112. if (!best || logit > best.logit) {
  113. // evidence: strongest positive n-grams within the winning span
  114. // (display-only approximation under hash collisions; score is exact)
  115. let topGrams = [];
  116. if (norm > 0) {
  117. for (const [g, { idx, sign }] of gramMeta) {
  118. const pair = m.weights[String(idx)];
  119. if (!pair) continue;
  120. const c = (sign * pair[0] * pair[1]) / norm;
  121. topGrams.push({ gram: g, contribution: c });
  122. }
  123. topGrams = topGrams.filter(t => t.contribution > 0)
  124. .sort((x, y) => y.contribution - x.contribution).slice(0, 3);
  125. }
  126. best = { logit, norm, topGrams, span: [a, b] };
  127. }
  128. }
  129. const score = 1 / (1 + Math.exp(-best.logit));
  130. return { score, logit: best.logit, topGrams: best.topGrams, tokens: tokens.length, span: best.span };
  131. }
  133. function snippetAround(text, needle) {
  134. const at = text.toLowerCase().indexOf(needle.toLowerCase());
  135. if (at < 0) return needle;
  136. const start = Math.max(0, at - 40);
  137. const end = Math.min(text.length, at + needle.length + 40);
  138. return (start > 0 ? '…' : '') + text.slice(start, end) + (end < text.length ? '…' : '');
  139. }
  141. /** Words reconstructed from the winning token window, for evidence snippets. */
  142. function spanText(text, span) {
  143. if (!span || span[1] - span[0] >= 10000) return String(text);
  144. const toks = String(text).toLowerCase().match(TOKEN_RE) || [];
  145. return toks.slice(span[0], span[1]).join(' ');
  146. }
  148. /**
  149. * Scan untrusted fields ([{field, text}]) with the model.
  150. * Returns null when no artifact is deployed; otherwise:
  151. * { threshold, suspect, best: {field, score, topGrams, snippet} | null,
  152. * perField: [{field, score}] }
  153. */
  154. export function scanInjectionModel(fields) {
  155. const m = getInjectionModel();
  156. if (!m) return null;
  157. const threshold = m.threshold;
  158. const suspect = Math.min(0.6, threshold * 0.55);
  159. const perField = [];
  160. let best = null;
  161. for (const { field, text } of fields) {
  162. if (!text || typeof text !== 'string') continue;
  163. const r = scoreInjectionText(text);
  164. if (!r) continue;
  165. perField.push({ field, score: r.score });
  166. if (!best || r.score > best.score) {
  167. const winText = spanText(text, r.span);
  168. best = {
  169. field, score: r.score, topGrams: r.topGrams,
  170. snippet: r.topGrams.length ? snippetAround(winText, r.topGrams[0].gram) : winText.slice(0, 90),
  171. };
  172. }
  173. }
  174. return { threshold, suspect, best, perField };
  175. }