Security Economics for Agentic Patching
ROI models backed by verified pass/fail data from 136 CVE samples and 1,664 test runs. Cost-per-fix analysis across 13 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.
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 from CVE-Agent-Bench.
What does a verified fix cost?
Costs range from $2.64 to $76.54 per automated fix across 13 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.
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 1,664 evaluations.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,664 evaluations.
Agent Cost Economics
Fix vulnerabilities for $2.64–$52 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 128 real vulnerabilities. Deterministic, reproducible, open methodology.
Agent Environment Security
AI agents run with real permissions. XOR verifies tool configurations, sandbox boundaries, and credential exposure.
Security Economics for Agentic Patching
Security economics for agentic patching. ROI models backed by verified pass/fail data and business-impact triage.
Validation Process
25 questions we ran against our own data before publishing. Challenges assumptions, explores implications, extends findings.
Cost Analysis
10 findings on what AI patching costs and whether it is worth buying. 1,664 evaluations analyzed.
Bug Complexity
128 vulnerabilities scored by difficulty. Floor = every agent fixes it. Ceiling = no agent can.
Agent Strategies
How different agents approach the same bug. Strategy matters as much as model capability.
Execution Metrics
Per-agent session data: turns, tool calls, tokens, and timing. See what happens inside an agent run.
Pricing Transparency
Every cost number has a source. Published pricing models, measurement methods, and provider rates.
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.
Getting Started with XOR GitHub App
Install in 2 minutes. First result in 15. One-click GitHub App install, first auto-review walkthrough, and engineering KPI triad.
Platform Capabilities
One install. Seven capabilities. Prompt-driven. CVE autopatch, PR review, CI hardening, guardrail review, audit packets, and more.
Dependabot Verification
Dependabot bumps versions. XOR verifies they're safe to merge. Reachability analysis, EPSS/KEV enrichment, and structured verdicts.
Compliance Evidence
Machine-readable evidence for every triaged vulnerability. VEX statements, verification reports, and audit trails produced automatically.
Compatibility and Prerequisites
Languages, build systems, CI platforms, and repository types supported by XOR. What you need to get started.
Command Reference
Every @xor-hardener command on one page. /review, /describe, /ask, /patch_i, /issue_spec, /issue_implement, and more.
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.
Agentic Third-Party Risk
33% of enterprise software will be agentic by 2028. 40% of those projects will be canceled due to governance failures. A risk overview for CTOs.
MCP Server Security
17 attack types across 4 surfaces. 7.2% of 1,899 open-source MCP servers contain vulnerabilities. Technical deep-dive with defense controls.
How Agents Get Attacked
20% jailbreak success rate. 42 seconds average. 90% of successful attacks leak data. Threat landscape grounded in published research.
Governing AI Agents in the Enterprise
92% of AI vendors claim broad data usage rights. 17% commit to regulatory compliance. Governance frameworks from NIST, OWASP, EU CRA, and Stanford CodeX.
OWASP Top 10 for Agentic Applications
The OWASP Agentic Top 10 mapped to real-world attack data and XOR capabilities. A reference page for security teams.
See which agents produce fixes that work
128 CVEs. 13 agents. 1,664 evaluations. Agents learn from every run.