Head-to-Head Comparison
AI Security Agent — AI application security agent with threat modeling and sandbox exploit validation, research preview (Mar 2026)
| Capability | DryRun Security | Codex | Verdict |
|---|---|---|---|
| AI & Intelligence7 | |||
| AI-Native Architecture | ✓ AI-native since 2023; model-independent; multi-agent agentic system (Code Review Agent, DeepScan Agent, Custom Policy Agent, Codebase Insight Agent) |
✓ AI-native; frontier GPT-5.4 model with sandbox-based exploit validation |
Tie |
| Business Logic Flaw Detection | ✓ IDOR, broken auth, multi-tenant isolation, logic flaws, mass assignment, privilege escalation, TOCTOU race conditions, OAuth failures, WebSocket auth bypass; 88% detection OOTB; outperformed 5 leading SAST tools (2025 SAST Accuracy Report) |
✓ Logic flaws via threat-model-guided scanning; reasons about code intent and behavior |
Tie |
| Contextual / Semantic Code Analysis | ✓ Contextual Security Analysis (CSA): data flow, architecture, change history, intent, exploitability; detects issues pattern-based SAST cannot — middleware defined but not mounted, trust boundary misalignment, config not wired up; reads AGENTS.md |
✓ Builds project-specific threat model; understands system behavior and context; cross-file data flows |
Tie |
| Vulnerability Coverage Breadth | ✓ 48+ vulnerability categories: SQLi, XSS, SSRF, IDOR, RCE, auth bypass, CSRF, XXE, path traversal, prompt injection, LLM tool misuse, OAuth failures, TOCTOU, WebSocket auth bypass, and more |
✓ Broad — guided by per-project threat model; any vuln the model can reason about |
Tie |
| Git Behavioral Analysis | ✓ Git Behavioral Graphs: code churn, temporal coupling, knowledge decay, temporal anomalies, intent mining |
✓ Analyzes commit history for vulnerability patterns |
Tie |
| Natural Language Policies | ✓ Natural Language Code Policies (NLCP); Policy Library with 16+ pre-built policies; Custom Policy Agent enforces on every PR |
~ Editable threat model functions as policy-like customization. Not a formal NL policy engine. |
DryRun leads |
| False Positive Reduction | ✓ 90% lower noise; CSA-driven reasoning; Risk Register dismissal with fingerprinting suppresses FPs in future scans |
✓ 50-84% noise reduction; threat model tuning + sandbox exploit validation |
Tie |
| AI Coding Agent Security6 | |||
| Securing AI-Generated Code | ✓ Reviews all code equally — human or AI-generated; model-independent verification layer; Agentic Coding Security Report (Mar 2026): 143 issues found across Claude/Codex/Gemini builds, 87% of PRs had vulns |
✓ Positioned for AI-generated code review; threat-model-driven scanning |
Tie |
| Malicious AI Agent Skill Detection | ✓ Policy Library includes Malicious AI Agent Skills Detection: flags skills/plugins that could enable data theft, backdoors, or code execution |
✗ | DryRun leads |
| MCP Integration | ✓ DryRun Insights MCP server: security summaries, PR analysis, trend monitoring, file-level history; connects via Direct HTTP, Claude Shortcuts, or mcp-remote |
~ MCP support in Codex agent. Security product MCP integration not documented. |
DryRun leads |
| AI Coding Tool Integrations | ✓ Native integrations: Cursor, Codex, Claude Code, Windsurf, VS Code (via Insights MCP + Add Skill); reviews output of any AI tool via PR workflow |
✓ Built into Codex/ChatGPT ecosystem; via Responses API |
Tie |
| AI Coding Visibility / Observability | ✓ Code Insights with AI Assistance (beta): NL queries for risk, trends, exposure; org-wide visibility; per-repo drill-down; file-level security history |
~ Tracks findings across scanned commits; learning loop from feedback |
DryRun leads |
| AI Red Teaming / Threat Modeling | ✗ | ✓ First-class editable threat model capturing attack surfaces, trust boundaries. |
Competitor leads |
| Code Security Intelligence3 | |||
| Code Security Knowledge Graph | ✓ Accumulates organizational knowledge across PRs; cross-repo intelligence; learns risk tolerance from dismissal patterns (nitpicks, FPs, accepted risks); FP fingerprinting improves decision quality over time |
~ Editable threat model captures entry points, trust boundaries, sensitive data paths. |
DryRun leads |
| Model-Independent Verification | ✓ Separates code generation from code verification; works regardless of which AI model or human generates code |
~ Sandboxed PoC execution adds non-model validation layer. |
DryRun leads |
| Continuous Baseline & Risk Trending | ✓ Risk Register with Critical/High/Medium/Low severity; AI Assistance for Insights with NL queries, trend monitoring, and 30-day window analysis |
~ Commit-by-commit scanning, but no trending dashboard. |
DryRun leads |
| Core Detection6 | |||
| SAST (Static Analysis) | ✓ AI-native Contextual Security Analysis engine; agentic multi-agent architecture; works on human and AI-generated code alike |
~ AI reasoning over code, not traditional SAST. Does not use fuzzing or signature scanning. |
DryRun leads |
| DAST (Dynamic Analysis) | ✗ | ~ Validates findings in sandboxed execution environments. Not a traditional DAST but has dynamic validation. |
Competitor leads |
| SCA (Dependency / Supply Chain) | ✓ SCA agent with dependency and supply chain analysis; Risk Register tracks SCA findings by severity |
✗ | DryRun leads |
| Secrets Detection | ✓ AI-native secrets analyzer; detects obfuscated secrets (concatenation, base64, logging); hard-coded credentials policy in Policy Library |
✗ | DryRun leads |
| IaC Scanning | ✓ IaC scanning (Terraform, YAML, and infrastructure-as-code analysis) |
✗ | DryRun leads |
| Container Scanning | ✗ | ✗ | Tie |
| Remediation & Fixes3 | |||
| Auto-Fix / AI Remediation | ✓ Tessl remediation skill for AI coding tools: extracts finding, researches authoritative sources, applies context-grounded fixes in the developer's codebase; co-authored commits; works in Cursor, Claude Code, Codex, VS Code |
✓ Contextual patches aligned with system behavior; proposes fixes for each finding |
Tie |
| Fix Verification / Re-testing | ✓ Re-runs DryRun Security analysis after remediation is applied to verify the fix resolves the finding |
✓ Post-patch revalidation closes the loop — re-validates after patching. |
Tie |
| Finding Dismissal & Triage Workflow | ✓ Risk Register with structured dismissal: Accepted Risk, False Positive, In Progress, Resolved, Won't Fix / Nitpick; learns risk tolerance of the repo and org from dismissal patterns (nitpicks, FPs, accepted risks); developer dismissal from PR comments (GitHub + GitLab) |
✗ (research preview; limited triage features) |
DryRun leads |
| Developer Workflow5 | |||
| PR / Merge Request Reviews | ✓ Every PR; real-time contextual feedback; pass/fail checks; inline explanations; reads AGENTS.md for project context |
~ Via general Codex agent, not dedicated security review Action like Claude. |
DryRun leads |
| Full Repository / Deep Scan | ✓ DeepScan Agent: full-repo security review in hours; discovers root and nested AGENTS.md for context; findings flow to Risk Register |
✓ Full codebase scanning; scanned 1.2M+ commits; 14 CVEs assigned |
Tie |
| IDE Integration | ✓ DryRun Insights MCP integrates with VS Code, Cursor, Windsurf, Claude Code, and Codex for security-aware coding assistance |
~ Runs in Codex web platform; rich IDE extensions but Security product is web-only |
DryRun leads |
| CI/CD Integration | ✓ GitHub and GitLab native integration; webhook notifications (Slack + generic) |
~ Codex GitHub Action exists but Security-specific CI/CD not documented. |
DryRun leads |
| SCM Support | GitHub and GitLab (native apps with OAuth) | GitHub only. | Tie |
| Coverage2 | |||
| Language Support | ✓ 15+ languages optimized: Python, JS/TS, Ruby, Go, C#, Java, Kotlin, PHP, Swift, Elixir, HTML, IaC (Terraform, YAML) |
✓ Broad language support via frontier GPT-5.4 model (any language the model can reason about) |
Tie |
| Out-of-Box Accuracy (No Tuning) | ✓ 88% detection rate OOTB; 2x more accurate than nearest competitor in independent testing |
✓ High precision; sandbox exploit validation ensures accuracy; threat model tuning |
Tie |
| Reporting & Compliance3 | |||
| Security Dashboard / Analytics | ✓ Risk Register (Critical/High/Medium/Low); AI Assistance for Insights with NL queries; Codebase Insight Agent; per-repo and file-level drill-down |
~ Basic findings interface, but no analytics or reporting. |
DryRun leads |
| Compliance / Audit Readiness | ~ Audit-ready reporting; policy enforcement evidence; structured finding dismissals with reasons and context |
✗ Research preview; non-deterministic; no compliance features. |
DryRun leads |
| SBOM / AI-BOM Generation | ✓ DeepScan generates SBOM; SCA agent provides dependency inventory and license checking (Dependency License Check policy) |
✗ | DryRun leads |
| Architecture & Positioning4 | |||
| Agentic / Multi-Agent System | ✓ Code Review Agent, Custom Policy Agent, DeepScan Agent, Codebase Insight Agent + specialized sub-agents; AGENTS.md support (Linux Foundation) |
✓ Autonomous pipeline from scan to validation to patch; not formal multi-agent but agentic |
Tie |
| API / Extensibility | ✓ DryRun Simple API (REST); Swagger/OpenAPI spec; webhook integrations (Slack + generic); MCP server |
~ Codex agent API via Responses API; Security-specific API not published. |
DryRun leads |
| Approach / Category | ℹ Code Security Intelligence: continuous, model-independent layer that understands, evaluates, and enforces code security for both human and AI-generated code; used to benchmark Claude, Codex, and Gemini security (Agentic Coding Security Report, Mar 2026) |
ℹ AI application security agent with threat modeling + exploit validation; research preview (Mar 2026); built on GPT-5.4 |
— |
| Key Structural Differentiator | ℹ Durable knowledge graph + model-independent verification: accumulates proprietary data about code behavior, vuln patterns, and org risk posture; proven benchmarking tool for AI coding agent security (Agentic Coding Security Report, Mar 2026) |
ℹ 14 CVEs assigned (GnuTLS, GOGS, Thorium, gpg-agent); unique sandbox exploit validation; formal editable threat model Claude lacks |
— |
| Market Feedback (G2)4 | |||
| G2 Rating / Review Count | ℹ 4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews |
ℹ No G2 reviews exist for OpenAI Codex |
— |
| Notable G2 Praise (Attributed) | ℹ "DryRun goes far beyond what rule-based SAST tools offer. It catches things other tools completely miss — like middleware that's defined but never mounted, or trust boundary misalignments." — Jabez A., Director, Product Security Architecture, Enterprise (g2.com/products/dryrun-security/reviews) |
ℹ No G2 reviews exist for OpenAI Codex Security product |
— |
| Notable G2 Criticisms (Attributed) | ℹ "I do somewhat wish there were more customization options for tuning the analyzers, but that seems to be in the works." — Kyle R. (g2.com/products/dryrun-security/reviews) |
ℹ "Learning curve for advanced features." (g2.com/products/openai/reviews) |
— |
| Common G2 Complaint Themes | ℹ UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews) |
ℹ Learning curve; expensive higher tiers; context hallucinations (g2.com/products/openai/reviews) |
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