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AuraquanTech

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Why 87% of Security Findings Never Get Fixed (And How We Solved It)

#security #opensource #devsecops #devops

The Security Theater Problem

Your security scanner just flagged 847 findings. Your developers will fix exactly 12 of them. Next week, the scanner finds 913 issues. Your team fixes 8. The cycle repeats.

This isn't laziness. This isn't negligence. This is the predictable outcome of a broken system.

Why Developers Ignore Security Findings

After analyzing 20,000+ pull requests and interviewing hundreds of developers, we found three consistent patterns:

1. The Context Gap

Security scanners show you what is wrong. They rarely show you where it matters or how to fix it within your actual codebase. A developer looking at "SQL Injection vulnerability detected" needs to understand:

  • Which specific query is vulnerable
  • What user input can reach it
  • How to fix it without breaking existing functionality
  • Whether this fix will pass code review

2. The False Positive Tax

When 60-80% of findings are false positives or irrelevant to your context, developers learn to ignore all findings. It's a rational response to an irrational signal-to-noise ratio.

3. The Friction Problem

Even when developers want to fix issues, the path of least resistance is to:

  • Mark it as "won't fix"
  • Add it to the backlog (where it dies)
  • Create a ticket (that nobody will prioritize)

The actual fix requires context switching, research, testing, and review. Most security findings aren't urgent enough to justify that cognitive load.

The Evidence-Based Approach

We built a different system. Instead of just flagging problems, we:

1. Generate Actual Fixes

Our remediation engine analyzes your codebase and generates ready-to-merge pull requests with:

  • Context-aware fixes that match your code style
  • Automated tests to prevent regressions
  • Documentation explaining the vulnerability and the fix

2. Confidence Scoring

Not all findings are equal. We score each one based on:

  • Reachability: Can untrusted input actually reach this code?
  • Exploitability: Is this theoretically vulnerable or practically exploitable?
  • Impact: What's the blast radius if this is exploited?
  • Fix Quality: How confident are we in the generated fix?

Only high-confidence findings become pull requests. Everything else gets explained but not automated.

3. Developer-First Design

The remediation happens in the developer's existing workflow:

# Before: Security finding in dashboard
Finding ID: SEC-4721
Severity: HIGH
File: api/auth.py
Issue: Hardcoded secret detected

# After: Pull request in your inbox
PR #842: Remove hardcoded API key from auth module
✅ Fix verified
✅ Tests passing
✅ Zero-trust secret management configured
📚 Learn more: [Why hardcoded secrets are dangerous]
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Real Results

After deploying this to our first 50 design partners:

  • 87% → 94% fix rate for high-confidence findings
  • 3 days → 4 hours median time to fix
  • 60% reduction in false positive noise
  • Zero additional meetings or process changes required

The Code: Evolutionary Remediation Engine

We open-sourced the entire system. It includes:

Template Engine: Generates language-specific fixes based on vulnerability patterns

# Example: SQL injection fix template
template = RemediationTemplate(
    vulnerability="sql_injection",
    language="python",
    fix_strategy="parameterized_query",
    confidence_threshold=0.85
)
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Evidence Analyzer: Scores findings based on multiple signals

scorer = ConfidenceScorer()
score = scorer.evaluate(
    reachability=reachability_analysis(code),
    exploitability=exploit_complexity(vuln),
    impact=blast_radius(code_context),
    fix_quality=test_coverage(generated_fix)
)
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PR Generator: Creates ready-to-merge pull requests

pr = PRGenerator(
    finding=security_finding,
    fix=generated_fix,
    tests=automated_tests,
    docs=vulnerability_explanation
)
pr.create(repo="your-org/your-repo")
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Try It Yourself

🔗 GitHub: github.com/AuraquanTech/evolutionary-remediation-engine

Quick Start 

git clone https://github.com/AuraquanTech/evolutionary-remediation-engine
cd evolutionary-remediation-engine
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Run analysis
python analyze.py --repo /path/to/your/repo

# Generate fixes
python remediate.py --findings findings.json --create-pr
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Design Partners Get Free Premium Forever

We're looking for 100 design partners to help us:

  • Test the engine on real-world codebases
  • Provide feedback on fix quality
  • Help prioritize language/framework support

In exchange:

  • Free premium features forever (no bait-and-switch)
  • Direct line to our engineering team
  • Your use case prioritized in our roadmap
  • Co-marketing opportunities if you want them

Interested? Drop a comment or reach out directly.

The Bottom Line

Security findings don't get fixed because the system is optimized for detection, not remediation. We built a system optimized for the opposite: making fixes so easy that ignoring them requires more effort than merging them.

The code is open source. The approach is evidence-based. The results speak for themselves.

What's your biggest blocker to fixing security findings? Let's discuss in the comments.

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