Security Code Review: Tools and Best Practices for Teams

Code review has been a software engineering practice since the 1970s, but its effectiveness as a security measure depends entirely on what reviewers look for. Most manual code reviews focus on logic correctness, readability, and architecture—security is a secondary concern unless the reviewer has security expertise.

Studies from the Software Engineering Institute show that manual code review catches approximately 60% of defects, but that rate drops to under 20% for security vulnerabilities because most developers are not trained to spot injection patterns, cryptographic weaknesses, or access control flaws during a standard review.

Automated code review fills this gap by applying hundreds of security rules consistently across every line of every pull request. Unlike human reviewers, automated tools do not experience fatigue, time pressure, or knowledge gaps in specific vulnerability classes.

The most effective approach combines both: automated tools catch known vulnerability patterns deterministically, while human reviewers focus on business logic, design decisions, and context-specific risks that tools cannot assess. This layered approach gives teams comprehensive coverage without overloading reviewers with tasks that machines do better.

Certain vulnerability classes appear frequently in pull requests because they arise from common coding patterns:

Injection Flaws

SQL injection, XSS, and command injection occur whenever user input is concatenated into queries, HTML output, or shell commands without sanitization. These patterns are easy to write and easy to miss in review:

// Easy to miss in a large PR
res.send('<div>' + req.query.name + '</div>');  // CWE-79: XSS

Hardcoded Secrets

API keys, database passwords, and tokens committed in source code are a persistent problem. Developers add them during development and forget to remove them before committing. A single hardcoded AWS key can compromise an entire cloud infrastructure.

Insecure Dependencies

Adding or updating a dependency in package.json, requirements.txt, or pom.xml can introduce known CVEs into the project. Dependency changes in pull requests rarely receive the same scrutiny as application code changes.

Cryptographic Misuse

Using MD5 for hashing, hardcoding encryption keys, or implementing custom cryptography instead of using established libraries. These patterns compile and run correctly, making them invisible to functional tests.

Automated security review on pull requests works by running SAST analysis on the code changes in each PR and reporting findings directly in the PR interface. This approach has several advantages over scheduled scans:

• Contextual feedback: Findings appear inline on the specific lines that introduced them, not in a separate report that developers must cross-reference.
• Blocking capability: The PR check can be configured to block merge when Critical or High severity issues are found, preventing vulnerable code from reaching the main branch.
• Incremental analysis: Only the changed files are analyzed, so developers see findings relevant to their work, not the entire codebase backlog.
• Audit trail: Every PR carries a record of security findings and their resolution, satisfying compliance requirements for evidence of security review.

The GitHub ecosystem supports this through status checks, pull request comments, and the GitHub Security Tab (via SARIF upload). Teams can enforce security gates without changing their existing development workflow.

The CodeSlick GitHub App installs in one click and runs automatically on every pull request. No configuration files, no pipeline changes, no infrastructure required.

• Automatic PR analysis: Every pull request triggers a security scan across all changed files in JavaScript, TypeScript, Python, Java, and Go
• Inline findings: Vulnerabilities appear as PR comments on the specific lines that introduced them, with CWE classification and CVSS severity
• SARIF upload: Findings upload to the GitHub Security Tab for centralized vulnerability tracking across repositories
• Pass/fail thresholds: Configurable severity thresholds block PRs that introduce Critical or High findings
• AI fix suggestions: Each finding includes a suggested fix, reducing the time from detection to resolution

For teams that want pre-commit scanning, the CodeSlick CLI catches issues before code is pushed, providing a second layer of defense before the PR stage.