graph-evolution

Graph Evolution

Builds Trailmark code graphs at two source snapshots and computes a structural diff. Surfaces security-relevant changes that text-level diffs miss: new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications.

When to Use

• Comparing two git refs to understand what structurally changed
• Auditing a range of commits for security-relevant evolution
• Detecting new attack paths created by code changes
• Finding functions whose blast radius or complexity grew silently
• Identifying taint propagation changes across refactors
• Pre-release structural comparison (tag-to-tag or branch-to-branch)

When NOT to Use

• Line-level code review (use differential-review for text-diff analysis)
• Single-snapshot analysis (use the trailmark skill directly)
• Diagram generation from a single snapshot (use the diagramming-code skill)
• Mutation testing triage (use the genotoxic skill)

Rationalizations to Reject

Rationalization	Why It's Wrong	Required Action
"We just need the structural diff, skip pre-analysis"	Without pre-analysis, you miss taint changes, blast radius growth, and privilege boundary shifts	Run engine.preanalysis() on both snapshots
"Text diff covers what changed"	Text diffs miss new attack paths, transitive complexity shifts, and subgraph membership changes	Use structural diff to complement text diff
"Only added nodes matter"	Removed security functions and shifted privilege boundaries are equally dangerous	Review removals and modifications, not just additions
"Low-severity structural changes can be ignored"	INFO-level changes (dead code removal) can mask removed security checks	Classify every change, review removals for replaced functionality
"One snapshot's graph is enough for comparison"	Single-snapshot analysis can't detect evolution — you need both before and after	Always build and export both graphs
"Tool isn't installed, I'll compare manually"	Manual comparison misses what graph analysis catches	Install trailmark first

---

Prerequisites

trailmark must be installed. If uv run trailmark fails, run:

uv pip install trailmark

DO NOT fall back to "manual comparison" or reading source files as a substitute for running trailmark. The tool must be installed and used programmatically. If installation fails, report the error.

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Quick Start

# Compare two git refs (e.g., tags, branches, commits)
# 1. Build graphs at each snapshot
# 2. Run pre-analysis on both
# 3. Compute structural diff
# 4. Generate report

# Step-by-step: see Workflow below

---

Decision Tree

├─ Need to understand what each metric means?
│  └─ Read: references/evolution-metrics.md
│
├─ Need the report output format?
│  └─ Read: references/report-format.md
│
├─ Already have two graph JSON exports?
│  └─ Jump to Phase 3 (run graph_diff.py directly)
│
└─ Starting from two git refs?
   └─ Start at Phase 1

---

Workflow

Graph Evolution Progress:
- [ ] Phase 1: Create snapshots (git worktrees)
- [ ] Phase 2: Build graphs + pre-analysis on both snapshots
- [ ] Phase 3: Compute structural diff
- [ ] Phase 4: Interpret diff and generate report
- [ ] Phase 5: Clean up worktrees

Phase 1: Create Snapshots

Use git worktrees to get clean copies of each ref without disturbing the working tree.

# Create temp directories for worktrees
BEFORE_DIR=$(mktemp -d)
AFTER_DIR=$(mktemp -d)

# Create worktrees (run from repo root)
git worktree add "$BEFORE_DIR" {before_ref}
git worktree add "$AFTER_DIR" {after_ref}

If comparing two directories instead of git refs, skip this phase and use the directory paths directly in Phase 2.

Phase 2: Build Graphs and Run Pre-Analysis

Build Trailmark graphs for both snapshots and run pre-analysis on each. Pre-analysis computes blast radius, taint propagation, privilege boundaries, and entrypoint enumeration.

import json
from trailmark.query.api import QueryEngine

def build_and_export(target_dir, language, output_path):
    """Build graph, run pre-analysis, export JSON."""
    engine = QueryEngine.from_directory(target_dir, language=language)
    engine.preanalysis()
    json_str = engine.to_json()
    with open(output_path, "w") as f:
        f.write(json_str)
    return engine.summary()

import tempfile, os
work_dir = tempfile.mkdtemp(prefix="trailmark_evolution_")
before_json = os.path.join(work_dir, "before_graph.json")
after_json = os.path.join(work_dir, "after_graph.json")

before_summary = build_and_export(
    "{before_dir}", "{lang}", before_json
)
after_summary = build_and_export(
    "{after_dir}", "{lang}", after_json
)

Verify both graphs built successfully by checking the summary output. If either fails, check that the language parameter matches the codebase and that trailmark supports all file types present.

Phase 3: Compute Structural Diff

Run the diff script on the two exported JSON files (using the same work_dir from Phase 2):

uv run {baseDir}/scripts/graph_diff.py \
    --before "{before_json}" \
    --after "{after_json}" > "{work_dir}/evolution_diff.json"

The output JSON contains:

Key	Contents
summary_delta	Changes in node/edge/entrypoint counts
nodes.added	New functions, classes, methods
nodes.removed	Deleted functions, classes, methods
nodes.modified	Functions with changed CC, params, return type, span
edges.added	New call/inheritance/import relationships
edges.removed	Deleted relationships
subgraphs	Per-subgraph membership changes (tainted, high_blast_radius, etc.)

Phase 4: Interpret Diff and Generate Report

Read the diff JSON and generate a security-focused markdown report. See references/report-format.md for the full template.

Interpretation priorities (highest to lowest):

1. New tainted paths — nodes entering the tainted subgraph, especially if they also appear in added edges targeting sensitive functions
2. Privilege boundary changes — new or removed trust transitions
3. Attack surface growth — new entrypoints, especially untrusted_external
4. Blast radius increases — nodes entering high_blast_radius
5. Complexity spikes — CC increases > 3 on tainted or entrypoint-reachable nodes
6. Structural additions — new nodes and edges (review needed)
7. Structural removals — verify removed security functions were replaced

Cross-reference structural changes with git diff {before_ref}..{after_ref} to add source-level context to findings.

Severity classification:

Severity	Structural Signal
CRITICAL	New tainted path to sensitive function, removed auth boundary
HIGH	New entrypoint + high blast radius, large CC increase on tainted node
MEDIUM	New trust-boundary-crossing edges, moderate CC increase
LOW	Added nodes without entrypoint reachability
INFO	Dead code removal, complexity reductions

For detailed metric definitions, see references/evolution-metrics.md.

Phase 5: Clean Up

Remove git worktrees after the report is written:

git worktree remove "{before_dir}"
git worktree remove "{after_dir}"

---

Diff Script Reference

uv run {baseDir}/scripts/graph_diff.py [OPTIONS]

Argument	Default	Description
--before	required	Path to the "before" graph JSON
--after	required	Path to the "after" graph JSON
--indent	2	JSON output indentation

Input format: Trailmark JSON exports from engine.to_json(). Output: JSON structural diff to stdout.

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Quality Checklist

Before delivering the report:

• Both graphs built successfully (check summaries)
• Pre-analysis ran on both snapshots
• Structural diff computed (non-empty diff JSON)
• All subgraph changes interpreted (tainted, blast radius, etc.)
• Critical findings include evidence (node IDs, edge diffs)
• Severity levels assigned to all findings
• Source-level context added via git diff cross-reference
• Worktrees cleaned up (or temp dirs removed)
• Report written to GRAPH_EVOLUTION_*.md

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Integration

trailmark skill: Phase 2 uses the trailmark API for graph building and pre-analysis. All trailmark query patterns work on either snapshot's engine.

differential-review skill: Use graph-evolution for structural analysis, differential-review for line-level code review. The two are complementary — graph-evolution finds attack paths that text diffs miss, while differential-review provides git blame context and micro-adversarial analysis.

genotoxic skill: If graph-evolution reveals new high-CC tainted nodes, feed them to genotoxic for mutation testing triage.

diagramming-code skill: Generate before/after diagrams to visualize structural changes. Use call-graph or data-flow diagrams focused on changed nodes.

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Supporting Documentation

• references/evolution-metrics.md — What each structural metric means and why it matters for security
• references/report-format.md — Report template, severity classification, and example findings