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ToolAccuracy of FindingsDetects Non-Pattern-Based Issues?Coverage of SAST FindingsSpeed of ScanningUsability & Dev Experience
DryRun SecurityVery high – caught multiple critical issues missed by othersYes – context-based analysis, logic flaws & SSRFBroad coverage of standard vulns, logic flaws, and extendableNear real-time PR feedback
Snyk CodeHigh on well-known patterns (SQLi, XSS), but misses other categoriesLimited – AI-based, focuses on recognized vulnerabilitiesGood coverage of standard vulns; may miss SSRF or advanced auth logic issuesFast, often near PR speedDecent GitHub integration, but rules are a black box
GitHub Advanced Security (CodeQL)Very high precision for known queries, low false positivesPartial – strong dataflow for known issues, needs custom queriesGood for SQLi and XSS but logic flaws require advanced CodeQL experience.Moderate to slow (GitHub Action based)Requires CodeQL expertise for custom logic
SemgrepMedium, but there is a good community for adding rulesPrimarily pattern-based with limited dataflowDecent coverage with the right rules, can still miss advanced logic or SSRFFast scansHas custom rules, but dev teams must maintain them
SonarQubeLow – misses serious issues in our testingLimited – mostly pattern-based, code quality orientedBasic coverage for standard vulns, many hotspots require manual reviewModerate, usually in CIDashboard-based approach, can pass “quality gate” despite real vulns
Vulnerability ClassSnyk (partial)GitHub (CodeQL) (partial)SemgrepSonarQubeDryRun Security
SQL Injection
*
Cross-Site Scripting (XSS)
SSRF
Auth Flaw / IDOR
User Enumeration
Hardcoded Token
ToolAccuracy of FindingsDetects Non-Pattern-Based Issues?Coverage of C# VulnerabilitiesScan SpeedDeveloper Experience
DryRun Security
Very high – caught all critical flaws missed by others
Yes – context-based analysis finds logic errors, auth flaws, etc.
Broad coverage of OWASP Top 10 vulns plus business logic issuesNear real-time (PR comment within seconds)Clear single PR comment with detailed insights; no config or custom scripts needed
Snyk CodeHigh on known patterns (SQLi, XSS), but misses logic/flow bugsLimited – focuses on recognizable vulnerability patterns
Good for standard vulns; may miss SSRF or auth logic issues 
Fast (integrates into PR checks)Decent GitHub integration, but rules are a black box (no easy customization)
GitHub Advanced Security (CodeQL)Low - missed everything except SQL InjectionMostly pattern-basedLow – only discovered SQL InjectionSlowest of all but finished in 1 minuteConcise annotation with a suggested fix and optional auto-remedation
SemgrepMedium – finds common issues with community rules, some missesPrimarily pattern-based, limited data flow analysis
Decent coverage with the right rules; misses advanced logic flaws 
Very fast (runs as lightweight CI)Custom rules possible, but require maintenance and security expertise
SonarQube
Low – missed serious issues in our testing
Mostly pattern-based (code quality focus)Basic coverage for known vulns; many issues flagged as “hotspots” require manual review Moderate (runs in CI/CD pipeline)Results in dashboard; risk of false sense of security if quality gate passes despite vulnerabilities
Vulnerability ClassSnyk CodeGitHub Advanced Security (CodeQL)SemgrepSonarQubeDryRun Security
SQL Injection (SQLi)
Cross-Site Scripting (XSS)
Server-Side Request Forgery (SSRF)
Auth Logic/IDOR
User Enumeration
Hardcoded Credentials
VulnerabilityDryRun SecuritySemgrepGitHub CodeQLSonarQubeSnyk Code
1. Remote Code Execution via Unsafe Deserialization
2. Code Injection via eval() Usage
3. SQL Injection in a Raw Database Query
4. Weak Encryption (AES ECB Mode)
5. Broken Access Control / Logic Flaw in Authentication
Total Found5/53/51/51/50/5
VulnerabilityDryRun SecuritySnykCodeQLSonarQubeSemgrep
Server-Side Request Forgery (SSRF)
(Hotspot)
Cross-Site Scripting (XSS)
SQL Injection (SQLi)
IDOR / Broken Access Control
Invalid Token Validation Logic
Broken Email Verification Logic
DimensionWhy It Matters
Surface
Entry points & data sources highlight tainted flows early.
Language
Code idioms reveal hidden sinks and framework quirks.
Intent
What is the purpose of the code being changed/added?
Design
Robustness and resilience of changing code.
Environment
Libraries, build flags, and infra metadata flag, infrastructure (IaC) all give clues around the risks in changing code.
KPIPattern-Based SASTDryRun CSA
Mean Time to Regex
3–8 hrs per noisy finding set
Not required
Mean Time to Context
N/A
< 1 min
False-Positive Rate
50–85 %< 5 %
Logic-Flaw Detection
< 5 %
90%+
Severity
CriticalHigh
Location
utils/authorization.py :L118
utils/authorization.py :L49 & L82 & L164
Issue
JWT Algorithm Confusion Attack:
jwt.decode() selects the algorithm from unverified JWT headers.
Insecure OIDC Endpoint Communication:
urllib.request.urlopen called without explicit TLS/CA handling.
Impact
Complete auth bypass (switch RS256→HS256, forge tokens with public key as HMAC secret).
Susceptible to MITM if default SSL behavior is weakened or cert store compromised.
Remediation
Replace the dynamic algorithm selection with a fixed, expected algorithm list. Change line 118 from algorithms=[unverified_header.get('alg', 'RS256')] to algorithms=['RS256'] to only accept RS256 tokens. Add algorithm validation before token verification to ensure the header algorithm matches expected values.
Create a secure SSL context using ssl.create_default_context() with proper certificate verification. Configure explicit timeout values for all HTTP requests to prevent hanging connections. Add explicit SSL/TLS configuration by creating an HTTPSHandler with the secure SSL context. Implement proper error handling specifically for SSL certificate validation failures.
Key Insight
This vulnerability arises from trusting an unverified portion of the JWT to determine the verification method itself
This vulnerability stems from a lack of explicit secure communication practices, leaving the application reliant on potentially weak default behaviors.
DryRun Security News
January 6, 2026

DryRun Security Builds Momentum With Breakthroughs in AI-Native Code Security Intelligence

DryRun Security, the industry’s first AI-native, code security intelligence company, has completed its first year out of stealth with strong corporate momentum. Over the past twelve months, the company delivered major product innovations, industry-leading vulnerability research and laid the groundwork for securing autonomous software development in the age of agentic AI.

“Modern software development has evolved dramatically, with autonomous agents and vibe coding quickly taking shape across the industry,” said James Wickett, CEO and co-founder, DryRun Security. “The momentum we achieved in our first year out of stealth is a reflection of the broader shift in how software is built. As AI agents take on more responsibility in coding workflows, security must become contextual, proactive and embedded directly into the development process. DryRun Security has built the foundation for that future.”

Early last year, DryRun Security closed an $8.7 million seed funding round, accelerating investment in product development, go-to-market expansion, and customer success. Enterprise and mid-market adoption is accelerating, with customers running more than 250,000 code reviews every month with DryRun Security, more than any other AI-native code security intelligence provider.

Product Innovation Built for Agentic Development

Over the last twelve months, DryRun Security doubled down on product innovation to address a growing gap in traditional application security tools. The company’s AI-native Contextual Security Analysis (CSA) engine was purpose-built to support agentic code security intelligence, delivering security that understands code behavior, execution context and autonomous decision-making across both human-driven and AI-driven workflows.

Powered by this core technology, DryRun Security introduced the following innovations:

  • Natural Language Code Policies (NLCPs): allows security teams to define secure coding requirements in plain English. These policies remove the complexity of rule-based configuration and enable faster alignment between security intent and real-world development practices, an essential capability for governing autonomous coding agents. Policies are no longer ignored in an old share site, but live in every pull request.
  • Custom Policy Agent: enforces natural language policies directly within developer workflows, scanning every pull request and providing inline, actionable feedback. Acting as an autonomous security guardrail, the agent helps ensure that both human developers and AI coding agents operate within approved security boundaries.
  • Code Insights MCP: securely connects DryRun’s Code Insights to MCP-compatible AI assistants, enabling natural language search, summaries, and trend reporting across pull requests and repositories. This gives security and engineering leaders fast visibility into high-risk changes, emerging patterns, and audit-ready evidence, without living in yet another dashboard.

Industry-Leading SAST Accuracy Validates Contextual Security Approach

DryRun Security’s contextual analysis approach delivers measurable accuracy gains. In the 2025 SAST Accuracy Report, DryRun detected 88% of seeded vulnerabilities out of the box, outperforming five leading static analysis tools, particularly on complex logic and authorization flaws. These results further validate why DryRun’s AI-native approach is essential as applications grow more complex and less deterministic, especially in AI-rich environments.

LLM & Agentic Applications Expose AppSec Blind Spots

The implications of these findings are even more pronounced in LLM-powered and agentic applications. In its research report, “Building Secure AI Applications,” DryRun Security found that more than 80% of vulnerabilities in LLM-enabled applications go undetected by traditional static analysis tools.

As execution paths become dynamic and code is increasingly generated or modified by autonomous agents, the shortcomings of legacy AppSec approaches are amplified, creating new classes of risk that demand a fundamentally different security model.

“As we lean harder into AI-generated code and highly customized delivery environments for our customers, we need more than a traditional code scanner. DryRun Security lets us continuously understand and explain the security posture of what we’re building, internally and for Fortune 50 clients, in a way that actually maps to how modern engineering teams work,” said Patrick McKinney, Vice President Security, Invisible Technologies. “The combination of real-time, context-aware analysis and MCP capabilities gives us a path to turn raw findings into customer-ready artifacts and ongoing assurance. For us, DryRun Security is less ‘AI code review’ and more a core piece of how we’re building an AI-first security program going into 2026 and beyond.”

About DryRun Security
DryRun Security is the industry’s first AI-native, agentic code security intelligence solution. Powered by its proprietary Contextual Security Analysis engine, DryRun Security helps security and developer teams reduce noise, surface real risk, and secure modern software built by both humans and autonomous agents. DryRun Security saves organizations thousands of hours otherwise spent on false positives, manual triage, and reactive reviews, while enabling security to scale with the speed and complexity of AI-driven development. For more information, please visit: https://www.dryrun.security/.