Security Economics for Agentic Patching
Security economics for agentic patching. ROI models backed by verified pass/fail data and business-impact triage.
ROI models for agentic vulnerability patching
OutcomeDecide if agent patching is worth the spend before you scale.
MechanismUse verified pass/fail data, cost-per-fix, and business-impact triage from CVE-Agent-Bench.
ProofReal cost data from 1,736 verified evaluations, not estimates.
Why security economics for agents
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.
ROI for verified patches
ROI is only as good as its inputs. XOR uses verified pass/fail data, not guesses, so security spend is tied to evidence.
What XOR measures
- Pass and fail rates across real vulnerabilities
- Cost per successful fix by agent
- Infra and build failure rates
- Time to verify and ship a safe fix
- Business impact triage for vulnerability prioritization
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.
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.
What the evidence says
- Pre-production fixes can be 100x cheaper than post-production fixes.
- Average time to triage, fix, and test a vulnerability is about 2 hours.
- A 100-developer team can spend about $700K per year on patching alone.
Sources: XOR Security Economics Inventory (Patched.codes 2024, HackerOne/NIST evidence).
Where the data comes from
Verified outcomes
XOR benchmark provides 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.
How does XOR calculate ROI?
ROI is based on verified outcomes: pass/fail rates, cost per fix, and comparison to manual incident response costs. Data from 1,736 evaluations.
Is agent patching cost-effective?
Pre-production fixes cost $2.64 to $87 via agents. Post-incident response costs thousands. Agent patching is 100x–1000x cheaper.
Patch verification
XOR writes a verifier for each vulnerability, then tests agent-generated patches against it. If the fix passes, it ships. If not, the failure feeds back into the agent harness.
Automated vulnerability patching
AI agents generate fixes for known CVEs. XOR verifies each fix and feeds outcomes back into the agent harness so future patches improve.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,736 evaluations.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,736 evaluations.
Agent Cost Economics
Fix vulnerabilities for $2.64–$87 with agents. 100x cheaper than incident response. Real cost data.
Agent Configurations
13 agent-model configurations evaluated on real CVEs. Compare Claude Code, Codex, Gemini CLI, Cursor, and OpenCode.
Benchmark Methodology
How CVE-Agent-Bench evaluates 13 coding agents on 136 real vulnerabilities. Deterministic, reproducible, open methodology.
Agent Environment Security
AI agents run with real permissions. XOR verifies tool configurations, sandbox boundaries, and credential exposure.
Automated Vulnerability Patching and PR Review
Automated code review, fix generation, GitHub Actions hardening, safety checks, and learning feedback. One-click install on any GitHub repository.
Continuous Learning from Verified Agent Runs
A signed record of every agent run. See what the agent did, verify it independently, and feed the data back so agents improve.
Signed Compliance Evidence for AI Agents
A tamper-proof record of every AI agent action. Produces evidence for SOC 2, EU AI Act, PCI DSS, and more. Built on open standards so auditors verify independently.
Compliance Evidence and Standards Alignment
How XOR signed audit trails produce evidence for SOC 2, EU AI Act, PCI DSS, NIST, and other compliance frameworks.
See which agents produce fixes that work
136 CVEs. 13 agents. 1,736 evaluations. Agents learn from every run.