DryRun Security Review 2026: AI SAST Built for Agentic Coding Workflows

DryRun Security is an AI-native SAST platform built specifically for teams shipping code with AI agents. Unlike traditional scanners that match patterns, it understands behavior — detecting logic-level flaws that Snyk, Semgrep, and CodeQL routinely miss.

What Is DryRun Security? (AI-Native SAST for the Agentic Era)

DryRun Security is an AI-powered Static Application Security Testing (SAST) platform designed from the ground up for agentic and AI-assisted coding workflows. Founded to address a specific failure mode — that traditional pattern-matching scanners cannot reason about code behavior, only code structure — DryRun built its Contextual Security Analysis (CSA) engine around large language models that understand intent, data flow, and business logic. In March 2026, DryRun published research showing 87% of AI agent pull requests (26 of 30 sampled) introduced at least one security vulnerability, and their CSA engine detected 88% of all seeded vulnerabilities in head-to-head testing — a figure that dropped below 40% for every competitor tested. DryRun earned a 4.9/5 rating on G2 and was named a High Performer in SAST in Spring 2026 G2 Reports. For teams running Claude Code, Cursor, or Windsurf, DryRun embeds directly into the IDE via its Code Insights MCP server, surfacing security findings before a PR is even opened.

The fundamental problem DryRun solves: AI-generated code is 1.88x more likely to introduce vulnerabilities than human-written code (SQ Magazine, 2026), and the attack surface is growing fast — agentic AI CVEs grew 255% year over year. Legacy SAST tools were built for human-written code with predictable patterns; they lack the semantic reasoning needed to catch what LLM-generated code produces.

Core Architecture: Contextual Security Analysis Engine Explained

The Contextual Security Analysis (CSA) engine is DryRun Security’s core differentiator — a reasoning layer built on a private, ephemeral LLM deployment that analyzes code behavior rather than matching it against known-bad patterns. CSA works by constructing a semantic representation of the code under review: it traces data flows across function boundaries, maps trust relationships between components, and evaluates business logic for violations like IDOR, broken authentication, and user enumeration — flaws that are invisible to regex-based or AST-pattern-based scanners. In the DryRun vs. Snyk/CodeQL/SonarQube/Semgrep Python/Django benchmark, CSA surfaced 88% of all planted vulnerabilities. Snyk Code caught approximately 38%, and the others landed below 50%. Critically, Snyk missed every logic-level flaw in the test set — not a partial miss, a complete miss — because pattern matchers have no model of what the code is supposed to do.

The privacy architecture matters here: DryRun runs its LLM in an isolated environment, with code analyzed ephemerally and never stored for model training. This makes it viable for regulated industries and enterprise teams with strict IP policies. The engine also generates natural-language explanations of each finding, which reduces triage time compared to raw scanner output. DryRun’s internal benchmark showed 143 security issues surfaced across 38 separate scans of AI-generated code — a volume that would overwhelm manual review but that CSA handled with consistent signal quality and low false-positive rates.

Key Features: Code Review Agent, DeepScan Agent, and Code Insights MCP

DryRun Security ships three primary surfaces: the Code Review Agent for PR-level scanning, the DeepScan Agent for full-repository security reviews, and the Code Insights MCP server for IDE-native security intelligence. The Code Review Agent is the day-to-day workhorse — it runs on every pull request, analyzing the diff plus surrounding context to find vulnerabilities introduced by the change. Unlike shallow diff-only scanners, it pulls in enough repository context to catch flaws that span files or depend on how a function is called downstream. Results appear as PR comments with severity ratings, remediation suggestions, and natural-language explanations — no security expertise required to act on them. This matters for AI-first teams where junior engineers or AI agents are shipping production code at volume.

The DeepScan Agent handles a different problem: comprehensive security assessment of an existing codebase. Traditional penetration testing for a medium-sized repository takes weeks; DryRun’s DeepScan delivers full-repository findings in hours. This is specifically useful for teams that inherited a codebase, recently migrated to AI-assisted development, or need to prepare for a compliance audit without scheduling a manual pentest. The GlobeNewswire announcement (February 2026) confirmed the hours-vs-weeks improvement was consistent across enterprise-scale codebases.

The Code Insights MCP server is arguably the most forward-looking feature. It exposes DryRun’s security intelligence as a Model Context Protocol tool that Claude, Cursor, Windsurf, and OpenAI Codex can call natively during code generation. When an AI coding agent writes a function, it can query DryRun’s MCP server for security context — catching the flaw before the file is even saved, let alone before it reaches a PR. This shifts security left past even the earliest conventional SAST gate.

DryRun Security vs. Competitors (Snyk, Semgrep, CodeQL, Checkmarx, SonarQube)

DryRun Security occupies a distinct position among SAST tools because it targets a failure mode — logic-level vulnerability detection in AI-generated code — that its competitors have not fully addressed. Snyk started as a Software Composition Analysis (SCA) tool and bolted on SAST; in the Python/Django showdown benchmark, Snyk Code caught approximately 38% of planted vulnerabilities and missed every logic-level flaw including IDOR, broken authentication, and user enumeration. Snyk remains the stronger choice for dependency-level vulnerability management, and the common enterprise pattern is Snyk for SCA paired with a dedicated SAST tool for code analysis. Semgrep uses a pattern-matching engine that covers 35+ languages and outperforms Snyk on pure SAST in EASE 2024 benchmarks, but it found less than half of known vulnerabilities in the same head-to-head tests. The operational overhead is also higher: teams must curate and maintain rule files, which creates drift as codebases evolve. DryRun’s own framing is direct: “Semgrep matches patterns; DryRun understands behavior.”

GitHub Advanced Security (CodeQL) offers deep integration for GitHub-hosted repositories and is strong on C/C++, Java, JavaScript, Python, and Go, but its analysis speed is better suited to comprehensive batch scans than PR-speed feedback. Checkmarx One is the enterprise platform that combines SAST, SCA, IaC, API, DAST, and container security into a unified product — it includes ASPM and agentic AI agents (Developer Assist), but the cost and configuration complexity put it out of reach for small and mid-sized teams. SonarQube is a popular open-source option with solid code quality + security coverage but showed weaker contextual and logic flaw detection against DryRun in comparative testing. DryRun’s advantage is narrow but decisive: for AI-generated code, logic-level flaw detection is the gap that matters most.

Real-World Detection Accuracy: 88% vs. 38% — What the Numbers Say

The 88% detection rate DryRun Security reports in its SAST Accuracy Report is not a vendor-run vanity benchmark — it comes from a structured test where known vulnerabilities were deliberately seeded across a Python/Django codebase and each tool was given identical access. DryRun’s CSA engine surfaced 88% of all seeded issues. Snyk Code found approximately 38%. Semgrep, CodeQL, and SonarQube all landed below 50%. The most revealing finding was not the top-line number but the category breakdown: every tool except DryRun missed the full set of logic-level flaws — IDOR, broken authentication, and user enumeration. These are exactly the vulnerabilities that AI agents produce at the highest rate, because LLMs tend to implement the happy path correctly while missing edge cases and trust boundaries.

The March 2026 Agentic Coding Security Report added field data: 143 security issues across 38 separate scans of AI-generated pull requests, with 87% of those PRs containing at least one exploitable flaw. That’s not a theoretical threat model — it’s a measurement of what happens when you deploy AI coding agents without behavioral security scanning. The 45% baseline rate (AI code introduces known security flaws — SQ Magazine 2026) and the 1.88x vulnerability multiplier compared to human code establish why a tool purpose-built for this problem class is the right answer rather than a tool originally designed for human-written code and extended to handle AI output.

False positive rate matters as much as detection rate: a tool that fires on every line produces alert fatigue that makes the signal worthless. DryRun’s natural-language explanations and context-aware findings give security teams and developers enough information to triage quickly, reducing the friction that causes teams to disable scanners entirely.

Agentic Coding Security Crisis: Why 87% of AI PRs Introduce Vulnerabilities

The agentic coding security crisis is a measurement problem before it is a tooling problem. Ninety-two percent of security professionals express concern about AI-driven security risks (SQ Magazine 2026), but the standard response — “we have SAST in CI” — assumes that SAST covers what AI agents produce. It does not. Pattern-matching tools were designed against a corpus of human-written vulnerability patterns; they do not generalize to the structural patterns LLMs produce when they implement authentication, authorization, or session management from a natural-language prompt. DryRun Security’s March 2026 report documented 87% of AI agent PRs (26 of 30) introducing at least one security vulnerability — and this was across agents using leading coding assistants, not fringe tools. The vulnerability categories skewed heavily toward logic flaws and broken authorization, precisely the categories that Snyk, Semgrep, and CodeQL miss.

The macro trend reinforces the urgency: agentic AI CVEs grew 255.4% year over year (from 74 to 263), and attacks exploiting application vulnerabilities rose 44% in 2026. The attack surface is expanding at the same time as the defenses are lagging. Teams that adopted AI-assisted development for productivity gains without updating their security tooling are accumulating technical security debt at machine speed. Prompt injection is an additional layer: 73% of AI systems assessed in 2026 security audits showed exposure to prompt injection vulnerabilities — and these require behavioral reasoning to detect, not pattern matching. DryRun’s CSA engine covers this category; legacy SAST tools do not.

MCP Integration: Bringing Security Intelligence into Claude, Cursor, and Windsurf

DryRun Security’s Code Insights MCP server is a direct integration with the Model Context Protocol ecosystem, which means AI coding assistants — Claude, Cursor, Windsurf, and OpenAI Codex — can call DryRun’s security analysis engine as a tool during code generation. This is a fundamentally different architecture from post-hoc scanning. In the conventional SAST workflow, code is written, committed, a PR is opened, and the scanner runs — the developer is now context-switched and the cost of fixing the finding is higher. With MCP integration, the AI coding agent queries DryRun before the code is finalized: “Does this implementation of session token validation have any security issues?” The MCP server responds with behavioral analysis, and the agent revises before output.

For teams running agentic workflows where a coding agent autonomously opens PRs, the MCP integration creates a self-correcting loop: the same agent that writes code can consult DryRun’s security intelligence and flag or fix its own vulnerabilities before human review. This pairs with the Code Review Agent at the PR layer, creating overlapping coverage. The MCP server uses the same ephemeral, private LLM infrastructure as the CSA engine — no code is retained between calls, and the analysis runs in isolation from external networks. Setup requires adding the DryRun MCP server endpoint to the IDE or agent configuration; DryRun documents this for Claude Code (via CLAUDE.md or settings), Cursor, and Windsurf specifically.

Pricing, Plans, and Who Should Use DryRun Security

DryRun Security does not publish a public pricing page with flat rates — pricing is usage-based and varies by repository count, team size, and which agents are enabled. From public sources and user reports, teams can expect a free trial tier that covers limited repository scanning, with paid plans scaling by the number of active repositories and monthly scan volume. The DeepScan Agent is typically an add-on or enterprise feature. Direct contact or a demo call is the standard path for pricing, which is consistent with enterprise security tooling. This is a friction point for teams that want to self-serve — Semgrep has a generous free tier and Snyk offers an open-source plan, so teams with cost constraints may trial those first before evaluating DryRun.

Who should use DryRun Security:

• Teams running Claude Code, Cursor, Windsurf, or any agentic coding workflow at meaningful velocity — this is DryRun’s native use case
• Engineering organizations that have adopted AI-assisted development and need to close the security gap without adding manual security review bandwidth
• AppSec teams responsible for securing AI-generated code across multiple product teams
• Companies in regulated industries where a privacy-preserving, ephemeral analysis model is a requirement
• Teams preparing for compliance audits who need full-codebase review faster than a traditional pentest allows

Who should consider alternatives:

• Small teams with primarily human-written code and strong pattern-based vulnerability profiles (Semgrep’s free tier covers this)
• Enterprises that need unified SCA + SAST + DAST + IaC in one platform (Checkmarx One is purpose-built for this)
• Teams where the primary risk is dependency vulnerabilities rather than logic flaws (Snyk remains strong for SCA)

Pros and Cons of DryRun Security

Pros:

• 88% detection rate in head-to-head benchmarks — the highest published figure in AI SAST
• Only tool tested that detects logic-level flaws (IDOR, broken auth, user enumeration) in AI-generated code
• Native MCP integration with Claude, Cursor, Windsurf, and OpenAI Codex
• Privacy-first architecture: ephemeral, private LLM — no code retained for training
• DeepScan Agent replaces weeks of manual pentest with hours of automated analysis
• Natural-language findings reduce triage friction for non-security engineers
• 4.9/5 G2 rating, High Performer designation Spring 2026

Cons:

• Pricing is not self-serve; requires demo or direct contact for enterprise plans
• Newer entrant — smaller community and fewer public integrations than Snyk or Semgrep
• Some teams prefer Snyk’s SCA depth for dependency management alongside SAST
• The MCP integration requires familiarity with MCP configuration, which adds setup complexity for less technical teams

Verdict: Is DryRun Security Worth It for AI-First Teams?

DryRun Security is worth it for any team running AI coding agents at non-trivial velocity. The 87% AI PR vulnerability rate is not a hypothetical — it is a measured rate in production codebases, and pattern-matching scanners miss the majority of what makes those PRs dangerous. DryRun’s 88% detection rate against competitors’ sub-50% figures, combined with its ability to catch logic-level flaws that no other tested tool found, represents a real security gap closed. The MCP integration is genuinely forward-looking: embedding behavioral security analysis inside the AI coding loop — not just after it — is the right architectural direction as agentic workflows move from experiment to default. For teams already using Claude Code, Cursor, or Windsurf, the integration story is the shortest path from “we have SAST” to “our SAST actually covers what we’re shipping.”

The pricing friction is a real concern for small teams, and Semgrep’s free tier remains a reasonable starting point for teams with limited AI-generated code volume. But for engineering organizations where AI agents are opening dozens or hundreds of PRs per week — and where 87% of those PRs statistically contain at least one vulnerability — a scanner catching 38% is meaningfully worse than one catching 88%. That gap translates directly to shipped vulnerabilities in production.

Bottom line: For AI-first teams, DryRun Security is the current best-in-class SAST tool for agentic coding workflows. The benchmark data is clear, the MCP integration is unique, and the privacy architecture removes the enterprise adoption blockers that AI-based tools often hit.

FAQ

The following questions cover the most common decision points for teams evaluating DryRun Security in 2026. DryRun Security is an AI-native SAST platform that uses a Contextual Security Analysis engine — not pattern matching — to detect behavioral and logic-level vulnerabilities in code. It is specifically designed for teams running agentic coding workflows with Claude Code, Cursor, Windsurf, or OpenAI Codex, where AI-generated pull requests introduce vulnerabilities at rates that traditional SAST tools cannot reliably catch. DryRun earned a 4.9/5 G2 rating and was named a High Performer in SAST in Spring 2026. Key differentiators include: 88% detection rate in benchmark testing (vs. sub-50% for Snyk, Semgrep, CodeQL, and SonarQube); native MCP integration for IDE-embedded security intelligence; DeepScan Agent for full-repository reviews in hours; and a privacy-first architecture using ephemeral, private LLM infrastructure that never retains source code. The five questions below address the most frequently asked topics around capabilities, comparisons, integrations, enterprise safety, and vulnerability coverage.

What is DryRun Security used for?

DryRun Security is a Static Application Security Testing (SAST) platform designed to detect vulnerabilities in AI-generated and human-written code. It specializes in logic-level flaws — IDOR, broken authentication, user enumeration — that pattern-matching tools miss. Teams use it for PR-level scanning, full-codebase security reviews via the DeepScan Agent, and IDE-native security intelligence via the Code Insights MCP server.

How does DryRun Security compare to Snyk for SAST?

In head-to-head testing, DryRun Security’s CSA engine detected 88% of seeded vulnerabilities versus Snyk Code’s approximately 38%. Snyk missed every logic-level flaw in the benchmark. Snyk remains stronger for Software Composition Analysis (dependency vulnerability management); many enterprise teams run both — Snyk for SCA, DryRun for SAST. For pure code vulnerability detection, especially in AI-generated code, DryRun substantially outperforms Snyk.

Does DryRun Security work with Claude Code and Cursor?

Yes. DryRun Security offers a Code Insights MCP server that integrates natively with Claude Code, Cursor, Windsurf, and OpenAI Codex. The MCP integration allows AI coding assistants to query DryRun’s security analysis engine during code generation — catching vulnerabilities before a PR is opened rather than after. This is the key architectural advantage over traditional SAST tools that only run post-commit.

Is DryRun Security safe for enterprise code — does it store my source code?

DryRun Security uses a private, ephemeral LLM deployment for its CSA engine. Code is analyzed in isolation and not stored or used for model training. This design was specifically chosen to address IP and compliance concerns in regulated industries. The analysis runs in an environment isolated from external networks, making it suitable for financial services, healthcare, and other industries with strict data handling requirements.

What types of vulnerabilities does DryRun Security detect?

DryRun Security detects the full OWASP Top 10 and goes beyond pattern-matching to find logic-level flaws including Insecure Direct Object Reference (IDOR), broken authentication, user enumeration, session management issues, prompt injection in AI systems, and authorization bypasses. It also covers standard vulnerability classes like SQL injection, XSS, and insecure deserialization. The CSA engine’s behavioral reasoning approach means it can detect novel vulnerability patterns produced by AI agents that do not match existing rule signatures.