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MRVA for CodeQL: A Business View
Introduction
The companion documents in this directory are mostly technical. The purpose of this document is to explain, from a business perspective, what MRVA is and why it matters.
To illustrate its impact, consider two real-world cases:
Case 1: Preventing Costly Security Failures
One of our customers faced a significant lawsuit due to inadequate security. The root cause? Unaddressed technical risks in their code. The work we do directly prevents similar vulnerabilities from reaching this stage.
While lawsuits of this scale are rare, security failures are not. More common consequences include:
- Compliance violations (e.g., GDPR, SOC2 penalties)
- Security breaches leading to reputation damage
- Productivity loss from disruptive technical failures
Lawsuits may be exceptional, but code security failures occur daily. Our role isn’t just about preventing catastrophic losses—it’s about avoiding the small, accumulating failures that erode security, compliance, and trust over time.
Case 2: Identifying Hidden Risks at Scale
Another customer manages a massive software portfolio of 120,000+ distinct codebases—a scale at which traditional security tools and manual review processes become impractical.
- A few known vulnerabilities had already been identified and patched.
- Our analysis uncovered 30 additional high-risk instances, previously undetected.
These findings were critical because:
- Traditional security tools break down at scale. Most solutions work well for isolated codebases but lack the capability to analyze patterns across 120,000 repositories.
- Complexity hides risk. Identifying these vulnerabilities required specialized techniques beyond simple scanning—capable of handling variations, context, and subtle exploit paths.
- Existing security processes failed to detect these vulnerabilities. Without proactive intervention, these risks would have remained undetected until a potential breach occurred.
This case highlights a critical gap in standard security practices. By leveraging advanced, scalable analysis, we identified and mitigated risks that would have otherwise gone unnoticed—demonstrating the value of proactive security at scale.
Why This Matters
These examples, along with others, reinforce the importance of proactive security—especially in the context of MRVA. Security risks don’t just exist in theory; they have tangible business consequences.
MRVA provides a scalable, systematic approach to identifying and addressing risks before they escalate—ensuring that security is a strategic advantage, not just a cost.
What is MRVA?
MRVA stands for Multi-Repository Variant Analysis. The concept is straightforward:
- A problem is identified in one codebase.
- Variations of this problem (variants) can be defined.
- The organization manages many code repositories (multi-repository).
- A systematic analysis is required to detect these variants across all repositories.
In practice:
- Steps 1 & 2: Defined through CodeQL queries, often custom-written for this purpose.
- Steps 3 & 4: Can be done manually but come with significant challenges.
Challenges of Manual Execution
Manually searching for these variants across multiple repositories is possible but inefficient and error-prone due to:
- High bookkeeping overhead – Tracking thousands of repositories is cumbersome.
- Heavy scripting requirements – Expert Unix scripting skills are necessary.
- Scaling limitations – Analyzing thousands of repositories sequentially is slow, and manual parallelization is impractical.
- Cumbersome review process – Results are stored as raw text files, requiring multiple processing steps for meaningful analysis.
MRVA: A Streamlined, Integrated Solution
Instead of relying on manual effort, MRVA is designed to automate and integrate the process.
- The system is designed to be machine-driven and integrated into an automated pipeline.
- Once incorporated, MRVA leverages the CodeQL VS Code plugin to provide a seamless user experience.
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How it works:
- Users submit queries through the UI.
- Results are retrieved and displayed dynamically as they become available.
- The entire workflow is automated, scalable, and significantly more efficient than manual methods.
By eliminating manual inefficiencies, MRVA enables organizations to identify and resolve security issues across massive codebases at scale, ensuring both accuracy and speed in vulnerability detection.