f418a8fe08
Full port of llm-security plugin for internal use on Windows with GitHub Copilot CLI. Protocol translation layer (copilot-hook-runner.mjs) normalizes Copilot camelCase I/O to Claude Code snake_case format — all original hook scripts run unmodified. - 8 hooks with protocol translation (stdin/stdout/exit code) - 18 SKILL.md skills (Agent Skills Open Standard) - 6 .agent.md agent definitions - 20 scanners + 14 scanner lib modules (unchanged) - 14 knowledge files (unchanged) - 39 test files including copilot-port-verify.mjs (17 tests) - Windows-ready: node:path, os.tmpdir(), process.execPath, no bash Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
58 lines
1.8 KiB
JavaScript
58 lines
1.8 KiB
JavaScript
// distribution-stats.mjs — Statistical divergence utilities for behavioral drift detection.
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// Zero external dependencies. <50 lines.
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//
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// Jensen-Shannon divergence measures how different two probability distributions are.
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// Used by post-session-guard.mjs to detect tool distribution shifts within a session.
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//
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// OWASP: ASI01 (Excessive Agency — behavioral pattern changes may indicate hijacking)
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/**
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* Kullback-Leibler divergence KL(P || Q).
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* @param {Map<string, number>} P
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* @param {Map<string, number>} Q
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* @returns {number}
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*/
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function klDivergence(P, Q) {
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let kl = 0;
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for (const [key, p] of P) {
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if (p === 0) continue;
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const q = Q.get(key) || 0;
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if (q === 0) return Infinity;
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kl += p * Math.log2(p / q);
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}
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return kl;
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}
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/**
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* Jensen-Shannon divergence. 0 = identical, 1 = fully disjoint (log2 basis).
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* Always finite, symmetric: JSD(P,Q) = JSD(Q,P).
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* @param {Map<string, number>} P - Normalized probability distribution
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* @param {Map<string, number>} Q - Normalized probability distribution
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* @returns {number}
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*/
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export function jensenShannonDivergence(P, Q) {
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const allKeys = new Set([...P.keys(), ...Q.keys()]);
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const M = new Map();
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for (const key of allKeys) {
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M.set(key, 0.5 * (P.get(key) || 0) + 0.5 * (Q.get(key) || 0));
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}
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return 0.5 * klDivergence(P, M) + 0.5 * klDivergence(Q, M);
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}
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/**
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* Build normalized probability distribution from category labels.
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* @param {string[]} labels
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* @returns {Map<string, number>} Values sum to 1.0 (empty input → empty map)
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*/
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export function buildDistribution(labels) {
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if (labels.length === 0) return new Map();
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const counts = new Map();
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for (const label of labels) {
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counts.set(label, (counts.get(label) || 0) + 1);
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}
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const dist = new Map();
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for (const [key, count] of counts) {
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dist.set(key, count / labels.length);
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}
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return dist;
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}
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