sarif-parsing

SARIF Parsing Best Practices

You are a SARIF parsing expert. Your role is to help users effectively read, analyze, and process SARIF files from static analysis tools.

When to Use

Use this skill when:

• Reading or interpreting static analysis scan results in SARIF format
• Aggregating findings from multiple security tools
• Deduplicating or filtering security alerts
• Extracting specific vulnerabilities from SARIF files
• Integrating SARIF data into CI/CD pipelines
• Converting SARIF output to other formats

When NOT to Use

Do NOT use this skill for:

• Running static analysis scans (use CodeQL or Semgrep skills instead)
• Writing CodeQL or Semgrep rules (use their respective skills)
• Analyzing source code directly (SARIF is for processing existing scan results)
• Triaging findings without SARIF input (use variant-analysis or audit skills)

SARIF Structure Overview

SARIF 2.1.0 is the current OASIS standard. Every SARIF file has this hierarchical structure:

sarifLog
├── version: "2.1.0"
├── $schema: (optional, enables IDE validation)
└── runs[] (array of analysis runs)
    ├── tool
    │   ├── driver
    │   │   ├── name (required)
    │   │   ├── version
    │   │   └── rules[] (rule definitions)
    │   └── extensions[] (plugins)
    ├── results[] (findings)
    │   ├── ruleId
    │   ├── level (error/warning/note)
    │   ├── message.text
    │   ├── locations[]
    │   │   └── physicalLocation
    │   │       ├── artifactLocation.uri
    │   │       └── region (startLine, startColumn, etc.)
    │   ├── fingerprints{}
    │   └── partialFingerprints{}
    └── artifacts[] (scanned files metadata)

Why Fingerprinting Matters

Without stable fingerprints, you can't track findings across runs:

• Baseline comparison: "Is this a new finding or did we see it before?"
• Regression detection: "Did this PR introduce new vulnerabilities?"
• Suppression: "Ignore this known false positive in future runs"

Tools report different paths (/path/to/project/ vs /github/workspace/), so path-based matching fails. Fingerprints hash the content (code snippet, rule ID, relative location) to create stable identifiers regardless of environment.

Tool Selection Guide

Strategy 1: Quick Analysis with jq

For rapid exploration and one-off queries:

# Pretty print the file
jq '.' results.sarif

# Count total findings
jq '[.runs[].results[]] | length' results.sarif

# List all rule IDs triggered
jq '[.runs[].results[].ruleId] | unique' results.sarif

# Extract errors only
jq '.runs[].results[] | select(.level == "error")' results.sarif

# Get findings with file locations
jq '.runs[].results[] | {
  rule: .ruleId,
  message: .message.text,
  file: .locations[0].physicalLocation.artifactLocation.uri,
  line: .locations[0].physicalLocation.region.startLine
}' results.sarif

# Filter by severity and get count per rule
jq '[.runs[].results[] | select(.level == "error")] | group_by(.ruleId) | map({rule: .[0].ruleId, count: length})' results.sarif

# Extract findings for a specific file
jq --arg file "src/auth.py" '.runs[].results[] | select(.locations[].physicalLocation.artifactLocation.uri | contains($file))' results.sarif

Strategy 2: Python with pysarif

For programmatic access with full object model:

from pysarif import load_from_file, save_to_file

# Load SARIF file
sarif = load_from_file("results.sarif")

# Iterate through runs and results
for run in sarif.runs:
    tool_name = run.tool.driver.name
    print(f"Tool: {tool_name}")

    for result in run.results:
        print(f"  [{result.level}] {result.rule_id}: {result.message.text}")

        if result.locations:
            loc = result.locations[0].physical_location
            if loc and loc.artifact_location:
                print(f"    File: {loc.artifact_location.uri}")
                if loc.region:
                    print(f"    Line: {loc.region.start_line}")

# Save modified SARIF
save_to_file(sarif, "modified.sarif")

Strategy 3: Python with sarif-tools

For aggregation, reporting, and CI/CD integration:

from sarif import loader

# Load single file
sarif_data = loader.load_sarif_file("results.sarif")

# Or load multiple files
sarif_set = loader.load_sarif_files(["tool1.sarif", "tool2.sarif"])

# Get summary report
report = sarif_data.get_report()

# Get histogram by severity
errors = report.get_issue_type_histogram_for_severity("error")
warnings = report.get_issue_type_histogram_for_severity("warning")

# Filter results
high_severity = [r for r in sarif_data.get_results()
                 if r.get("level") == "error"]

sarif-tools CLI commands:

# Summary of findings
sarif summary results.sarif

# List all results with details
sarif ls results.sarif

# Get results by severity
sarif ls --level error results.sarif

# Diff two SARIF files (find new/fixed issues)
sarif diff baseline.sarif current.sarif

# Convert to other formats
sarif csv results.sarif > results.csv
sarif html results.sarif > report.html

Strategy 4: Aggregating Multiple SARIF Files

When combining results from multiple tools:

import json
from pathlib import Path

def aggregate_sarif_files(sarif_paths: list[str]) -> dict:
    """Combine multiple SARIF files into one."""
    aggregated = {
        "version": "2.1.0",
        "$schema": "https://json.schemastore.org/sarif-2.1.0.json",
        "runs": []
    }

    for path in sarif_paths:
        with open(path) as f:
            sarif = json.load(f)
            aggregated["runs"].extend(sarif.get("runs", []))

    return aggregated

def deduplicate_results(sarif: dict) -> dict:
    """Remove duplicate findings based on fingerprints."""
    seen_fingerprints = set()

    for run in sarif["runs"]:
        unique_results = []
        for result in run.get("results", []):
            # Use partialFingerprints or create key from location
            fp = None
            if result.get("partialFingerprints"):
                fp = tuple(sorted(result["partialFingerprints"].items()))
            elif result.get("fingerprints"):
                fp = tuple(sorted(result["fingerprints"].items()))
            else:
                # Fallback: create fingerprint from rule + location
                loc = result.get("locations", [{}])[0]
                phys = loc.get("physicalLocation", {})
                fp = (
                    result.get("ruleId"),
                    phys.get("artifactLocation", {}).get("uri"),
                    phys.get("region", {}).get("startLine")
                )

            if fp not in seen_fingerprints:
                seen_fingerprints.add(fp)
                unique_results.append(result)

        run["results"] = unique_results

    return sarif

Strategy 5: Extracting Actionable Data

import json
from dataclasses import dataclass
from typing import Optional

@dataclass
class Finding:
    rule_id: str
    level: str
    message: str
    file_path: Optional[str]
    start_line: Optional[int]
    end_line: Optional[int]
    fingerprint: Optional[str]

def extract_findings(sarif_path: str) -> list[Finding]:
    """Extract structured findings from SARIF file."""
    with open(sarif_path) as f:
        sarif = json.load(f)

    findings = []
    for run in sarif.get("runs", []):
        for result in run.get("results", []):
            loc = result.get("locations", [{}])[0]
            phys = loc.get("physicalLocation", {})
            region = phys.get("region", {})

            findings.append(Finding(
                rule_id=result.get("ruleId", "unknown"),
                level=result.get("level", "warning"),
                message=result.get("message", {}).get("text", ""),
                file_path=phys.get("artifactLocation", {}).get("uri"),
                start_line=region.get("startLine"),
                end_line=region.get("endLine"),
                fingerprint=next(iter(result.get("partialFingerprints", {}).values()), None)
            ))

    return findings

# Filter and prioritize
def prioritize_findings(findings: list[Finding]) -> list[Finding]:
    """Sort findings by severity."""
    severity_order = {"error": 0, "warning": 1, "note": 2, "none": 3}
    return sorted(findings, key=lambda f: severity_order.get(f.level, 99))

Common Pitfalls and Solutions

1. Path Normalization Issues

Different tools report paths differently (absolute, relative, URI-encoded):

from urllib.parse import unquote
from pathlib import Path

def normalize_path(uri: str, base_path: str = "") -> str:
    """Normalize SARIF artifact URI to consistent path."""
    # Remove file:// prefix if present
    if uri.startswith("file://"):
        uri = uri[7:]

    # URL decode
    uri = unquote(uri)

    # Handle relative paths
    if not Path(uri).is_absolute() and base_path:
        uri = str(Path(base_path) / uri)

    # Normalize separators
    return str(Path(uri))

2. Fingerprint Mismatch Across Runs

Fingerprints may not match if:

• File paths differ between environments
• Tool versions changed fingerprinting algorithm
• Code was reformatted (changing line numbers)

Solution: Use multiple fingerprint strategies:

def compute_stable_fingerprint(result: dict, file_content: str = None) -> str:
    """Compute environment-independent fingerprint."""
    import hashlib

    components = [
        result.get("ruleId", ""),
        result.get("message", {}).get("text", "")[:100]

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