Security Economics for Agentic Patching
ROI models backed by verified pass/fail data from 128 CVE samples and 1,920 evaluations. Cost-per-fix analysis across 15 agent configurations.
Security ROI with real data
Agent fixes move fast, but the cost of a wrong fix is high. Security economics puts verified outcomes and business-impact triage into a decision model before you scale deployments.
What XOR measures
Pass and fail rates across real vulnerabilities, cost per successful fix by agent, time to verified patch, and regression risk from incomplete fixes.
Security ROI with real data
Security ROI = (risk reduced - cost) / cost. XOR replaces guesswork with tested pass/fail rates and cost-per-fix data from real bugs. Every agent evaluation produces a dollar figure: how much it costs to fix one vulnerability with that agent, what percentage of bugs it actually fixes, and whether the fix passes independent verification. These three numbers let you model ROI before committing budget.
Why this matters now
AI agent API costs add up fast. Most companies pay for them and have no data on what works. XOR gives you tested results so you can make budget decisions before you scale agent deployments. See benchmark economics for specific cost-per-fix numbers.
[EVIDENCE]
What the evidence says
- Pre-production fixes can be 100x cheaper than post-production fixes. A vulnerability caught before deployment costs $100-500 to fix. The same bug found in production by a customer costs $10,000-50,000 in incident response, reputational damage, and regulatory fines (NIST Cybersecurity Economics study).
- Average time to triage, fix, and test a vulnerability is about 2 hours of engineering labor at $150/hour. $300 per bug. At scale across 100 developers, that's 200 bugs per year, or $60K in labor alone.
- A 100-developer team can spend about $700K per year on patching alone. This includes labor, tools, CI infrastructure, and opportunity cost of context switching.
Sources: XOR Security Economics Inventory (Patched.codes 2024, HackerOne/NIST evidence).
Where the data comes from
Verified outcomes
XOR benchmark reports pass, fail, build, and infrastructure rates for each agent.
Cost per fix
Benchmark economics data shows API cost per fix and the best cost/accuracy trade-offs.
Who uses this data
Engineering leaders
Decide which agent to scale and what spend is justified before rollout.
Security leaders
Tie tested fix outcomes to risk reduction and audit-ready evidence.
Next steps
FAQ
What is agentic security economics?
A framework for measuring the cost and value of using AI agents to patch security vulnerabilities, backed by verified pass/fail data from CVE-Agent-Bench.
What does a verified fix cost?
Costs range from $2.64 to $76.54 per automated fix across 15 agent configurations. Manual triage costs $75-$600 per CVE at $150/hr fully loaded engineering cost.
How do costs change over time?
Costs decrease as verification coverage grows. Each triaged vulnerability adds a regression test to the suite, reducing unknowns on future CVEs.
How Verification Works
Test agents on real vulnerabilities before shipping fixes.
Automated Vulnerability Patching
AI agents generate fixes for known CVEs. XOR verifies each fix against the vulnerability before it ships.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,920 evaluations.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,920 evaluations.
Agent Cost Economics
Fix vulnerabilities for $2.64–$52 with agents. 100x cheaper than incident response. Real cost data.
Agent Configurations
15 agent-model configurations benchmarked on real vulnerabilities. Compare pass rates and costs.
See which agents produce fixes that work
128 CVEs. 15 agents. 1,920 evaluations. Agents learn from every run.