SAST/DAST integration in CI/CD pipelines

Sun, 12 May 2024 00:00:00 +0000

SAST vs DAST: Understanding the Difference

Application security testing has two fundamental approaches, and knowing when to use each matters for your security posture.

SAST (Static Application Security Testing) reads source code, bytecode, or binaries without running the app. It looks for patterns that match known vulnerabilities (SQL injection, XSS, hardcoded credentials, insecure deserialization). Think of it like a security linter that flags dangerous patterns.

DAST (Dynamic Application Security Testing) tests a running application by sending crafted requests and analyzing responses. It simulates what an attacker would do: probe endpoints, test authentication, look for misconfigurations. DAST sees the app from outside, like a real attacker would.

Aspect	SAST	DAST
When it runs	Build time, on source code	Runtime, against deployed app
What it finds	Code flaws, hardcoded secrets, insecure patterns	Runtime vulnerabilities, misconfigurations, auth issues
False positive rate	Higher (no runtime context)	Lower (tests real behavior)
Language dependency	Yes (needs language-specific rules)	No (tests HTTP/API layer)
Coverage	All code paths (including dead code)	Only reachable endpoints
Speed	Fast (seconds to minutes)	Slower (minutes to hours)
Best stage	Every commit/PR	Staging or pre-production

Short answer: use both. SAST catches problems early and cheap. DAST catches problems that only show up at runtime. They complement each other.

Tool comparison

SAST tools

Tool	Languages	Strengths	License
Semgrep	30+ languages	Fast, custom rules, great CI integration	OSS + Commercial
SonarQube	25+ languages	Broad ecosystem, quality gates, dashboard	Community + Commercial
Bandit	Python only	Python-specific, lightweight, easy setup	OSS (Apache 2.0)
CodeQL	10+ languages	Deep semantic analysis, GitHub native	Free for OSS

DAST tools

Tool	Type	Strengths	License
OWASP ZAP	Proxy-based scanner	Full-featured, scriptable, community rules	OSS (Apache 2.0)
Burp Suite	Proxy-based scanner	Best-in-class manual + automated testing	Commercial
Nuclei	Template-based scanner	Fast, huge template library, CI-friendly	OSS (MIT)

For most teams, Semgrep + OWASP ZAP provides an excellent open-source foundation that covers both SAST and DAST without licensing costs.

GitLab CI pipeline integration

Here is a complete .gitlab-ci.yml example that integrates both SAST and DAST into a pipeline with proper gating:

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stages:
 - build
 - test
 - sast
 - deploy-staging
 - dast
 - deploy-production

variables:
 SEMGREP_RULES: "p/owasp-top-ten p/security-audit"
 ZAP_TARGET_URL: "https://staging.example.com"

# --- SAST Stage ---
semgrep-scan:
 stage: sast
 image: semgrep/semgrep:latest
 script:
 - semgrep ci --config "$SEMGREP_RULES" --json --output semgrep-results.json
 - |
 # Fail pipeline if high/critical findings exceed threshold
 HIGH_COUNT=$(cat semgrep-results.json | jq '[.results[] | select(.extra.severity == "ERROR")] | length')
 echo "High severity findings: $HIGH_COUNT"
 if [ "$HIGH_COUNT" -gt 0 ]; then
 echo "Pipeline blocked: $HIGH_COUNT high severity findings"
 exit 1
 fi
 artifacts:
 paths:
 - semgrep-results.json
 when: always
 allow_failure: false

bandit-scan:
 stage: sast
 image: python:3.11-slim
 script:
 - pip install bandit
 - bandit -r src/ -f json -o bandit-results.json --severity-level medium || true
 - |
 HIGH_COUNT=$(cat bandit-results.json | jq '.results | map(select(.issue_severity == "HIGH")) | length')
 echo "Bandit high severity findings: $HIGH_COUNT"
 if [ "$HIGH_COUNT" -gt 3 ]; then
 exit 1
 fi
 artifacts:
 paths:
 - bandit-results.json
 when: always
 only:
 - merge_requests
 - main

# --- Deploy Staging ---
deploy-staging:
 stage: deploy-staging
 script:
 - echo "Deploying to staging..."
 - ./deploy.sh staging
 environment:
 name: staging
 url: https://staging.example.com

# --- DAST Stage ---
owasp-zap-scan:
 stage: dast
 image: ghcr.io/zaproxy/zaproxy:stable
 script:
 - mkdir -p /zap/wrk
 - |
 zap-full-scan.py \
 -t "$ZAP_TARGET_URL" \
 -r zap-report.html \
 -J zap-results.json \
 -l WARN \
 -d
 - |
 # Parse results and enforce policy
 HIGH_ALERTS=$(cat zap-results.json | jq '[.site[].alerts[] | select(.riskcode == "3")] | length')
 echo "High risk alerts: $HIGH_ALERTS"
 if [ "$HIGH_ALERTS" -gt 0 ]; then
 echo "Pipeline blocked: $HIGH_ALERTS high risk vulnerabilities found"
 exit 1
 fi
 artifacts:
 paths:
 - zap-report.html
 - zap-results.json
 when: always
 dependencies:
 - deploy-staging

# --- Deploy Production ---
deploy-production:
 stage: deploy-production
 script:
 - echo "Deploying to production..."
 - ./deploy.sh production
 environment:
 name: production
 when: manual
 only:
 - main

The key design decisions in this pipeline:

SAST runs on every merge request, catching issues before code merges
DAST runs against staging, testing the deployed application
Thresholds are configurable: zero tolerance for high/critical SAST findings, but some flexibility for lower severities
Reports are always saved as artifacts, even when the pipeline passes
Production deploy is manual, gated behind both SAST and DAST stages

Handling False Positives

False positives are the number one reason teams abandon security scanning. Handle them systematically:

For Semgrep

Create a .semgrepignore file or use inline annotations:

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# nosemgrep: python.lang.security.audit.hardcoded-password
DEFAULT_TEST_PASSWORD = "test123" # Only used in test fixtures

For persistent false positives, create a semgrep-exclusions.yml:

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rules:
 - id: ignore-test-passwords
 pattern: $X = "..."
 paths:
 exclude:
 - tests/
 - fixtures/

For OWASP ZAP

Use a context file to exclude known false positives:

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<alertFilter>
 <ruleId>10038</ruleId>
 <url>https://staging.example.com/api/health</url>
 <urlIsRegex>false</urlIsRegex>
 <enabled>true</enabled>
 <newRisk>-1</newRisk> <!-- -1 = False Positive -->
</alertFilter>

The golden rule: every suppression must include a justification comment explaining why it is safe to ignore. Review suppressions quarterly.

Threshold and Gate Policies

Define clear policies per environment and severity:

Severity	PR/MR Gate	Main Branch Gate	Pre-Production
Critical	Block	Block	Block
High	Block	Block	Warn
Medium	Warn	Warn	Info
Low	Info	Info	Info

Implement these as exit codes in your CI scripts. Start permissive and tighten over time – blocking on everything from day one will create frustration and workarounds.

Reporting and Dashboards

Aggregate results from both SAST and DAST into a centralized view:

DefectDojo: open-source vulnerability management platform that ingests reports from Semgrep, ZAP, Bandit, and dozens of other tools
GitLab Security Dashboard: native integration if you are on GitLab Ultimate
Custom dashboards: push scan results to Elasticsearch and visualize in Grafana

Track these metrics over time:

Mean time to remediate (MTTR) per severity
Total open vulnerabilities by age
False positive rate (suppressions vs total findings)
Scan coverage (percentage of repos with security scanning enabled)

IAST as a Complement

IAST (Interactive Application Security Testing) combines elements of both SAST and DAST by instrumenting the application at runtime. An agent runs inside the application during testing, correlating HTTP requests with code execution paths.

IAST tools like Contrast Security and Datadog Application Security provide:

Lower false positive rates than SAST
Code-level context that DAST lacks
Real-time feedback during QA testing

Consider IAST as a third layer once SAST and DAST are mature in your pipeline.

Practical Rollout Strategy

Deploying security scanning organization-wide requires phases:

Phase 1: Visibility (Weeks 1-4)

Enable SAST in CI with allow_failure: true (don’t block)
Generate reports, keep as artifacts
Find top vulnerability categories
Establish baseline metrics

Phase 2: Selective Gating (Weeks 5-8)

Block only critical/high SAST findings
Add DAST scans to staging for key apps
Create suppression and triage workflows
Train developers how to interpret and fix findings

Phase 3: Full Integration (Weeks 9-12)

Enforce SAST gates everywhere
Enforce DAST gates on all web apps
Integrate with vulnerability management platform
Auto-create tickets for findings

Phase 4: Continuous Improvement (Ongoing)

Write custom Semgrep rules for your patterns
Tune ZAP policies to reduce scan time
Track MTTR, set improvement targets
Quarterly reviews of false positives

Biggest mistake: jumping straight to Phase 3. Start with visibility, build trust, tighten gradually. Scanning should help, not block.

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