Dependabot Verification
Dependabot bumps versions. XOR verifies they're safe to merge. Reachability analysis, EPSS/KEV enrichment, and structured verdicts.
Automated triage
Every Dependabot PR gets: CVE parsing, reachability analysis via Semgrep and SCALIBR, EPSS/KEV/CVSS enrichment, and a structured verdict. No @xor-hardener mention needed.
Cost dynamics
Without XOR: 200+ Dependabot PRs triaged per month at 20-45 min each. With XOR: automatic triage, 70-80% noise filtered by reachability analysis, 13 min median time from CVE to merged fix.
Dependabot bumps versions. XOR verifies they're safe to merge.
Every Dependabot PR gets a structured triage: reachability analysis, exploitability assessment, EPSS/KEV enrichment, and a clear verdict. No manual triage. This runs automatically — no @xor-hardener mention needed.
How it works
Reachability analysis
XOR checks whether your code actually calls the vulnerable function — not just whether the dependency is present. Static analysis via Semgrep and SCALIBR against your repository's call graph. The analysis runs on XOR's read-only mirror, not on your infrastructure.
A typical org with 50 repos sees 200+ Dependabot PRs per month. 70-80% of flagged vulnerabilities are not reachable in the application's actual code paths. Reachability filtering eliminates that noise.
Exploitability scoring
Three public data sources, correlated:
EPSS
Probability of exploitation in next 30 days (0-1 scale)
CISA KEV
Already exploited in the wild
CVSS v3.1
Attack vector, complexity, privileges, impact
EXPLOITABLE: Reachable AND (EPSS > 0.1 OR in CISA KEV OR CVSS ≥ 7.0)
NOT EXPLOITABLE: Not reachable, or reachable but all risk indicators low
NEEDS REVIEW: Reachable but metrics are mixed — human judgment needed
Improved PRs
When the verdict is EXPLOITABLE, XOR opens a new PR superseding the Dependabot PR:
- Same version bump as Dependabot
- Regression test covering the vulnerable code path
- Pinned transitive dependencies if the advisory affects downstream packages
- Verification evidence: "Exploit reproduced pre-fix, fails post-fix"
Economic impact
Without XOR
200+ PRs/month triaged manually, 20-45 min each
Monthly cost: $10,000-$22,500 at $150/hr
With XOR
200+ PRs/month triaged automatically
70-80% noise filtered. 13 min median fix time.
Costs decrease as verification coverage grows. Each triaged vulnerability adds a regression test, reducing unknowns on future CVEs.
[NEXT STEPS]
Related documentation
FAQ
How does XOR work with Dependabot?
XOR intercepts every Dependabot PR automatically. It runs reachability analysis, enriches with EPSS/KEV/CVSS data, and posts a verdict: EXPLOITABLE, NOT EXPLOITABLE, or NEEDS REVIEW.
What is reachability analysis?
XOR checks whether your code actually calls the vulnerable function, not just whether the dependency is present. 70-80% of Dependabot alerts are not reachable in application code paths.
What happens when a vulnerability is exploitable?
XOR opens an improved PR that supersedes the Dependabot PR: same version bump, plus a regression test, pinned transitive dependencies, and verification evidence.
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.
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.