Abstract

Sophisticated adversaries implant subtle, malicious logic designed to be functionally indistinguishable from legitimate code and invisible to traditional differential analysis tools (e.g., git diff). This paper introduces 'Semantic Provenance,' a forensic methodology that detects these threats by analyzing the historical evolution of a program's logic via Abstract Syntax Tree (AST) comparison.

1. Introduction: The Inadequacy of Textual Analysis

Modern security relies on code review, comparing textual changes. This fails because it analyzes text, not intent. An adversary can introduce a backdoor by changing one operator (&& to ||) or reordering two lines—changes that appear trivial. Semantic Provenance moves beyond the surface by parsing every commit into a structured, logical representation (an AST) to flag suspicious shifts in logic.

2. The Methodology of Semantic Provenance

A three-stage pipeline to transform VCS history into a map of logical evolution.

2.1. Stage 1: Historical AST Generation

For each commit, the source code is parsed into its corresponding AST, a graph representation of the code that captures its logical structure, control flow, and data relationships, abstracting away superficial details.

2.2. Stage 2: Semantic Graph Differencing

Instead of comparing text files, we compare the ASTs of sequential commits to identify fundamental changes: Control Flow Modification, Data-Flow Manipulation, and Authorization Logic Inversion (e.g., is_authorized() to is_not_authorized()).

2.3. Stage 3: Heuristic-Based Anomaly Detection

A rules engine and ML model analyze the stream of semantic changes to identify high-risk patterns, like a security check moved to an ineffective position or a minor bug fix that introduces a subtle flaw elsewhere.

3. Case Study: The Reordered Conditional

A change from if (is_admin() && perform_action()) to if (perform_action() && is_admin()) seems innocuous. However, if perform_action() has a side effect, the action now executes before the authorization check. A textual diff flags this as a minor change; Semantic Provenance identifies it as a critical reordering of authorization and execution logic.

4. Conclusion: The Future of Code Assurance

Relying on human review and textual analysis is no longer defensible. Semantic Provenance provides a systematic, automated, and evidence-based capability to look beneath the surface of code and analyze its true logical integrity over time, identifying vulnerabilities designed to evade all conventional forms of analysis.