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

Use Case Tool Installation

Quick CLI queries jq brew install jq / apt install jq

Python scripting (simple) pysarif pip install pysarif

Python scripting (advanced) sarif-tools pip install sarif-tools

.NET applications SARIF SDK NuGet package

JavaScript/Node.js sarif-js npm package

Go applications garif go get github.com/chavacava/garif

Validation SARIF Validator sarifweb.azurewebsites.net

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],  # First 100 chars
]

# Add code snippet if available
if file_content and result.get("locations"):
    region = result["locations"][0].get("physicalLocation", {}).get("region", {})
    if region.get("startLine"):
        lines = file_content.split("\n")
        line_idx = region["startLine"] - 1
        if 0 <= line_idx < len(lines):
            # Normalize whitespace
            components.append(lines[line_idx].strip())

return hashlib.sha256("".join(components).encode()).hexdigest()[:16]

3. Missing or Incomplete Data

SARIF allows many optional fields. Always use defensive access:

def safe_get_location(result: dict) -> tuple[str, int]: """Safely extract file and line from result.""" try: loc = result.get("locations", [{}])[0] phys = loc.get("physicalLocation", {}) file_path = phys.get("artifactLocation", {}).get("uri", "unknown") line = phys.get("region", {}).get("startLine", 0) return file_path, line except (IndexError, KeyError, TypeError): return "unknown", 0

4. Large File Performance

For very large SARIF files (100MB+):

import ijson # pip install ijson

def stream_results(sarif_path: str): """Stream results without loading entire file.""" with open(sarif_path, "rb") as f: # Stream through results arrays for result in ijson.items(f, "runs.item.results.item"): yield result

5. Schema Validation

Validate before processing to catch malformed files:

Using ajv-cli

npm install -g ajv-cli ajv validate -s sarif-schema-2.1.0.json -d results.sarif

Using Python jsonschema

pip install jsonschema

from jsonschema import validate, ValidationError import json

def validate_sarif(sarif_path: str, schema_path: str) -> bool: """Validate SARIF file against schema.""" with open(sarif_path) as f: sarif = json.load(f) with open(schema_path) as f: schema = json.load(f)

try:
    validate(sarif, schema)
    return True
except ValidationError as e:
    print(f"Validation error: {e.message}")
    return False

CI/CD Integration Patterns

GitHub Actions

• name: Upload SARIF uses: github/codeql-action/upload-sarif@v3 with: sarif_file: results.sarif

• name: Check for high severity run: | HIGH_COUNT=$(jq '[.runs[].results[] | select(.level == "error")] | length' results.sarif) if [ "$HIGH_COUNT" -gt 0 ]; then echo "Found $HIGH_COUNT high severity issues" exit 1 fi

Fail on New Issues

from sarif import loader

def check_for_regressions(baseline: str, current: str) -> int: """Return count of new issues not in baseline.""" baseline_data = loader.load_sarif_file(baseline) current_data = loader.load_sarif_file(current)

baseline_fps = {get_fingerprint(r) for r in baseline_data.get_results()}
new_issues = [r for r in current_data.get_results()
              if get_fingerprint(r) not in baseline_fps]

return len(new_issues)

Key Principles

• Validate first: Check SARIF structure before processing

• Handle optionals: Many fields are optional; use defensive access

• Normalize paths: Tools report paths differently; normalize early

• Fingerprint wisely: Combine multiple strategies for stable deduplication

• Stream large files: Use ijson or similar for 100MB+ files

• Aggregate thoughtfully: Preserve tool metadata when combining files

Skill Resources

For ready-to-use query templates, see {baseDir}/resources/jq-queries.md:

• 40+ jq queries for common SARIF operations

• Severity filtering, rule extraction, aggregation patterns

For Python utilities, see {baseDir}/resources/sarif_helpers.py:

• normalize_path()

• Handle tool-specific path formats

• compute_fingerprint()

• Stable fingerprinting ignoring paths

• deduplicate_results()

• Remove duplicates across runs

Reference Links

• OASIS SARIF 2.1.0 Specification

• Microsoft SARIF Tutorials

• SARIF SDK (.NET)

• sarif-tools (Python)

• pysarif (Python)

• GitHub SARIF Support

• SARIF Validator