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
    │   ├── ruleIndex (index into tool.driver.rules[])
    │   ├── level (OPTIONAL, inherited from the rule when absent)
    │   ├── message.text
    │   ├── locations[]
    │   │   └── physicalLocation
    │   │       ├── artifactLocation.uri
    │   │       └── region (startLine, startColumn, etc.)
    │   ├── fingerprints{}
    │   └── partialFingerprints{}
    └── artifacts[] (scanned files metadata)

Severity Is Not Always on the Result

result.level is optional. CodeQL omits it on every result and records severity on the rule as defaultConfiguration.level, which the result inherits. Read result.level directly and a CodeQL run scores as clean however many errors it found, which is how a severity gate ends up exiting 0 on a failing repo.

Resolve severity in this order (SARIF 2.1.0 section 3.27.10):

1. kind other than "fail" (a pass/notApplicable record), so "none"
2. result.level, when present
3. the matched rule's defaultConfiguration.level, joining ruleIndex into runs[].tool.driver.rules[], or matching ruleId against rules[].id when the tool omits ruleIndex
4. "warning", the SARIF default

Every severity query in this skill starts from that resolution. In jq it is the LEVEL_FN definition in {baseDir}/resources/jq-queries.md; in Python it is resolve_level(result, run) in {baseDir}/resources/sarif_helpers.py.

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

# Severity resolution, needed by every query below that filters on level.
# See resources/jq-queries.md for the annotated version.
LEVEL_FN='
  def rule($run):
    . as $r
    | ($run.tool.driver.rules // []) as $rules
    | (if ($r.ruleIndex | type) == "number" and $r.ruleIndex >= 0
       then $rules[$r.ruleIndex] else null end)
      // first($rules[] | select(.id == $r.ruleId))
      // null;
  def level($run):
    . as $r
    | if ($r.kind // "fail") != "fail" then "none"
      else ($r.level // rule($run).defaultConfiguration.level // "warning") end;
'

# Extract errors only
jq "$LEVEL_FN"'.runs[] as $run | $run.results[] | select(level($run) == "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 "$LEVEL_FN"'[.runs[] as $run | $run.results[] | select(level($run) == "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:
        # pysarif fills a missing result.level with "warning", so .level here is NOT the
        # rule-inherited severity: a CodeQL error (no level on the result, severity on the
        # rule) reads as "warning". Gate severity with Strategy 1's level() or with
        # resolve_level() in resources/sarif_helpers.py, which resolve it from the rule.
        print(f"  {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 by severity. sarif-tools hands back raw result dicts, and a result's level may
# live on its rule, so resolve it against the run instead of reading r["level"].
from sarif_helpers import extract_findings, filter_by_level, load_sarif

high_severity = filter_by_level(extract_findings(load_sarif("results.sarif")), "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 sarif_helpers import deduplicate, extract_findings

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

unique = deduplicate(extract_findings(aggregate_sarif_files(["tool1.sarif", "tool2.sarif"])))

deduplicate() prefers whatever fingerprints or partialFingerprints the tool supplied and falls back to hashing rule ID, the whole normalized path, line, and message. Keep the directory in that key: the same rule at the same line in auth/login.py and admin/login.py is two findings, and a basename-only key throws one of them away.

Strategy 5: Extracting Actionable Data

resources/sarif_helpers.py covers this with the standard library alone. extract_findings() returns Finding objects whose severity is already resolved, and filter_by_level(), sort_by_severity(), deduplicate() and diff_findings() consume those:

from sarif_helpers import extract_findings, filter_by_level, load_sarif, sort_by_severity

findings = sort_by_severity(extract_findings(load_sarif("results.sarif")))
for f in filter_by_level(findings, "error"):
    print(f"{f.file_path}:{f.start_line} [{f.level}] {f.rule_id}: {f.message}")

Writing your own extractor, severity is the part that goes wrong silently:

def resolve_level(result: dict, run: dict) -> str:
    """Severity of a result: its own level, else its rule's default, else "warning"."""
    if result.get("kind", "fail") != "fail":
        return "none"
    if result.get("level"):
        return result["level"]

    rules = run.get("tool", {}).get("driver", {}).get("rules", [])
    index = result.get("ruleIndex")
    rule = rules[index] if isinstance(index, int) and 0 <= index < len(rules) else next(
        (r for r in rules if r.get("id") == result.get("ruleId")), {}
    )
    return rule.get("defaultConfiguration", {}).get("level") or "warning"

Results carry ruleIndex on some tools and only ruleId on others, so a resolver that joins one way alone silently returns the default for every result the other kind of tool produces.

Common Pitfalls and Solutions

1. Path Normalization Issues

Different tools report paths differently (absolute, relative, URI-encoded), so file:///src/a%20b.py and src/a b.py can be the same file. Strip the file:// scheme, percent-decode, resolve against a base path, and normalize separators before comparing or hashing anything: normalize_path() in resources/sarif_helpers.py does all four.

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  # run via: uv run --with 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
uv run --with jsonschema python your_script.py   # e.g. the function below

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: |
    # select(.level == "error") counts zero on CodeQL output, which records severity on
    # the rule instead. Resolve the level or the gate passes on a repo full of errors.
    HIGH_COUNT=$(jq '
      def rule($run):
        . as $r
        | ($run.tool.driver.rules // []) as $rules
        | (if ($r.ruleIndex | type) == "number" and $r.ruleIndex >= 0
           then $rules[$r.ruleIndex] else null end)
          // first($rules[] | select(.id == $r.ruleId))
          // null;
      def level($run):
        . as $r
        | if ($r.kind // "fail") != "fail" then "none"
          else ($r.level // rule($run).defaultConfiguration.level // "warning") end;
      [.runs[] as $run | $run.results[] | select(level($run) == "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

1. Validate first: Check SARIF structure before processing
2. Resolve severity, never read result.level: it is optional, and CodeQL always omits it
3. Handle optionals: Many fields are optional; use defensive access
4. Normalize paths: Tools report paths differently; normalize early
5. Fingerprint wisely: Combine multiple strategies for stable deduplication
6. Stream large files: Use ijson or similar for 100MB+ files
7. 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
• LEVEL_FN - the severity resolution every filtering query starts from
• Severity filtering, rule extraction, aggregation patterns

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

• resolve_level() - Severity from the result or the rule it inherits from
• normalize_path() - Handle tool-specific path formats
• compute_fingerprint() - Rule, normalized path, line, and message
• deduplicate() - Remove duplicates across runs

Two SARIF fixtures live in {baseDir}/resources/fixtures, one with severity on the rules only and one with severity on the results. Each contains exactly one error, so a gate can be checked against a known answer before it is trusted.

Reference Links

• OASIS SARIF 2.1.0 Specification
• Microsoft SARIF Tutorials
• SARIF SDK (.NET)
• sarif-tools (Python)
• pysarif (Python)
• GitHub SARIF Support
• SARIF Validator