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