Report: Is Corgea a Good SAST Vendor?

Overview

This report examines whether Corgea is a strong choice as a Static Application Security Testing (SAST) / AI-native code security vendor, based on public documentation, third-party writeups, and early user feedback.

It focuses on three practical questions:

How capable is Corgea’s platform at finding and fixing real vulnerabilities?
How does it compare to traditional and AI-native SAST tools?
What risks or gaps should a security team factor into an evaluation?

Note: Corgea is an emerging vendor. Evidence today comes mostly from vendor materials, partner articles, and a small but growing number of third‑party reviews rather than years of broad enterprise adoption.

How does Corgea’s AI SAST compare to legacy SAST on false positives? How does Corgea compare to GitHub Advanced Security? Which AI-native SAST vendors are strongest in 2025? How well does Corgea BLAST find business-logic flaws in practice? What are the risks of AI auto-fix in security tools, and how are they mitigated? How should we evaluate SAST tools for our environment?

What Corgea Actually Does

Positioning. Corgea markets itself as an AI-native application security platform that “automatically finds, triages, and fixes insecure code,” sitting on top of or alongside traditional SAST tools rather than simply replacing them in all cases. It targets both code scanning and automated remediation.

Core components (from vendor and third-party descriptions):

BLAST (Business Logic Application Security Testing): Corgea’s flagship AI-powered scanner combining LLMs + AST + static analysis to detect both traditional and business-logic vulnerabilities in source code, with emphasis on low false positives and semantic understanding of code.¹
Auto-fix / Auto-triage: AI-generated secure patches and explanations, delivered as pull requests for developer approval; marketed as reducing engineering fix effort by ~80% and cutting SAST findings (noise) significantly.²
Language support: Corgea claims 20+ languages including Java, JavaScript/TypeScript, Go, Ruby, Python, C#, C, C++, PHP, Kotlin and frameworks, with ongoing expansion.³
Integration model: Corgea can ingest findings from existing SAST tools (e.g., Semgrep, others) to de-duplicate, de-noise, and auto-fix, and it can also run its own scans (BLAST) for deeper semantic analysis.⁴

A Y Combinator profile describes Corgea as an AI-powered platform that writes security patches for engineer approval and “saves ~80% of the engineering effort” to fix vulnerabilities.⁵

Evidence It’s a Strong SAST / Code Security Option

1. Detection depth and business-logic coverage

Vendor materials and a detailed independent walkthrough suggest that BLAST is designed specifically to tackle business logic flaws and complex code paths that legacy SAST misses.

Corgea’s own whitepaper and docs claim BLAST uses LLMs with ASTs to achieve deeper semantic understanding of code, enabling detection of business logic flaws, broken authentication, and complex access-control issues.⁶⁷
Documentation for BLAST explicitly lists coverage of business logic vulnerabilities (CWE-840) and other logic / auth issues.⁸
A third-party article from “AppSec Untangled” walks through running BLAST on a deliberately vulnerable app and reports that BLAST successfully detected an IDOR / improper access-control flaw that is emblematic of business-logic issues.⁹

Overall, the available evidence supports that Corgea is meaningfully oriented toward business‑logic and semantic analysis, which is a differentiator against pattern-only SAST.

2. False positives and noise reduction

A recurring theme in both vendor content and external coverage is that Corgea aims to reduce SAST noise and alert fatigue.

Corgea’s product marketing and whitepapers claim that combining LLMs with AST-based static analysis allows BLAST to operate with <5% false positives, vs. traditional SAST tools where false-positive rates often exceed 20%.¹⁰¹¹
Corgea explicitly advertises that it can cut false positives by 30%+ relative to standalone SAST, in part by ingesting SAST results and running contextual false-positive detection and prioritization.¹²
An Infoworld piece on pairing SAST with AI (referencing Corgea’s approach) reports up to ~90% reduction in false positives when SAST results are fed through an LLM-based triage layer in real‑world tests.¹³
Corgea’s own guides on reducing SAST false positives acknowledge the chronic problem (traditional tools can produce up to 50% erroneous findings without good configuration) and position Corgea as a mitigation layer on top of existing scanners.¹⁴

While these figures are largely vendor- or partner-reported (not broad independent benchmarks), the directional evidence is consistent: Corgea’s value is less “we are another SAST rules engine” and more “we make SAST usable by drastically reducing noise and adding context-aware detection.”

3. Automated remediation (auto-fix) and developer UX

Corgea is also marketed as a fix engine, not just a scanner.

Vendor docs and aifordevelopers.io describe Corgea generating ready-to-merge pull requests that fix vulnerabilities such as SQL injection, path traversal, SSRF, XSS, and others across multiple languages, coupled with explanations for developers.¹⁵¹⁶
Corgea’s launch and YC writeups consistently quote ~80% reduction in developer time spent on remediating security findings, because engineers only review and approve / adjust proposed patches instead of hand-writing all fixes.⁵¹⁷
In a .NET example, a Semgrep-detected SQL injection is automatically fixed by Corgea, with both the code change and a human-readable explanation of the security issue.[^fix4]
Corgea’s Policy and CodeIQ features add policy-driven detection and tailored false-positive policies, which aim to further reduce noise and provide more precise fixes.¹⁸¹⁹

For security teams urgently trying to move from detection to remediation, Corgea’s auto‑fix capability is one of its clearest differentiators versus classic SAST-only vendors.

4. Third-party reviews and ecosystem recognition

Independent and semi-independent sources (blogs, curated lists, rankings) increasingly place Corgea among the top AI-native security tools:

A 2024/2025 review of AI SAST / security tools by an LLM engineer and pentester ranks ZeroPath, Corgea, and Almanax as the top three products in their tests for finding vulnerabilities, based on hands-on evaluation.²⁰
A popular curated list of static analysis tools describes Corgea as an AI tool that finds business-logic flaws and automatically writes security fixes, distinguishing it from pure linters and classic SAST engines.²¹
Press coverage (e.g., HelpNet Security, PR Newswire) frames BLAST as a “next-generation AI-driven SAST platform” with a focus on business‑logic vulnerability detection and automated code remediation, emphasizing its applicability in enterprise environments.²²²³
Corgea appears on shortlists of “best AI-powered SAST in 2025” and “top SAST tools” from some industry blogs and solution-review sites.²⁴²⁵

These signals are early but directionally positive: Corgea is being noticed and evaluated as a serious entry in the AI-native SAST / code security space.

Limitations, Risks, and Unknowns

Despite the promising capabilities, there are important caveats for a cautious buyer:

1. Early-stage vendor with limited long-term track record

Corgea is a young startup (YC-backed); there is no large corpus of public, multi-year enterprise case studies comparable to mature players like Snyk, Checkmarx, or Perforce/Klocwork.
Most positive data points come from vendor content, partner writeups, or a small number of engineer blogs. There is little public evidence yet of large, regulated enterprises running Corgea at scale over several years.

Implication: treat claims about false-positive rates, accuracy, and productivity gains as promising but not yet broadly validated. You should reproduce them via POCs and controlled rollouts in your own environment.

2. AI auto-fix and accuracy concerns

General limitations of AI-based code tools apply:

Research and practitioner guides on AI code review highlight that AI tools can generate false positives and may miss complex bug interactions or project-specific nuances without careful configuration and guardrails.²⁶
Community commentary around AI code-fix tools notes that LLM-based fixes may sometimes be “best guesses” that are syntactically correct but semantically wrong or incomplete, creating risk if merged without strong review.²⁷
Corgea itself acknowledges the need for policies and guardrails to avoid misclassification and ensure that false-positive management is tuned to the organization.¹⁸

Implication: you should still treat Corgea’s fixes as suggestions requiring code review, not as an autonomous patching system in production-critical repos.

3. Partial visibility into real-world performance

There are no large-scale, independent head-to-head benchmarks (e.g., MITRE-style evaluations) comparing Corgea vs. legacy SAST and other AI-native SAST vendors across standard test suites.
The strongest public “comparison” data comes from a single Latio / LLM engineer review and some vendor-provided metrics; while helpful, it’s not the same as an industry-wide bake‑off.
BLAST is still described as “private beta” in some documentation, which may mean feature churn, limited availability, or evolving quality.²⁸

Implication: for mission‑critical use, plan for careful POC design (golden repos with known vulns, regression tests, and side‑by‑side runs with existing tools) before broad rollout.

4. Coverage gaps outside code (cloud, infra, container, etc.)

Corgea is specifically focused on application source code and related business logic. It does not appear to be a full-stack security platform (e.g., CSPM, CNAPP, container scanning, IaC scanning for all environments) today.
For organizations looking for end‑to‑end platform consolidation, Corgea will still need to be paired with other tools for infrastructure, runtime, and supply-chain security.

Implication: It’s best thought of as a specialist for code and SAST+auto-fix rather than a one-stop security suite.

Comparative Lens: Corgea vs. Other SAST / AI-native Tools

The available public data allows only a high-level comparative view, but some axes are clear.

Capability snapshot

Capability / Claim	Corgea	Traditional SAST (generic)	Other AI-native SAST notes
Language coverage	20+ languages including Java, JS/TS, Go, Python, Ruby, C#, C/C++, PHP, Kotlin, etc.³	Varies; major tools cover main enterprise languages well	Several AI tools have narrower language sets focused on web stacks
Business-logic detection	Yes – BLAST marketed specifically for business logic, auth, access-control flaws; independent example shows IDOR detection.⁸⁹	Typically weak; rule-based, limited semantic understanding	Some AI-native competitors (e.g., Gecko, others) also emphasize logic flaws
False-positive handling	Claims <5% FP and 30–80%+ reduction in SAST findings/alerts via AI triage and de-duplication; can ingest other SAST outputs.¹⁰¹²¹³	Commonly 20–50%+ FPs without careful tuning.¹⁴	Other AI-backed tools also promise big FP reductions; head-to-head data is limited
Auto-fix	Yes – generates patches for common vulns (SQLi, SSRF, XSS, etc.) across languages, with explanations; marketed as ~80% time savings.¹⁵¹⁷	Rare or limited; usually code suggestions or manual remediation	Several AI tools have code-fix capabilities; quality varies and is under-tested
Maturity / track record	Early-stage, YC-backed startup; positive but limited ecosystem references.⁵²⁰	Many vendors with 5–10+ years in enterprise and broad references	Some AI-native peers are equally early-stage

Given the current evidence, Corgea looks competitive to strong in AI-native SAST and auto-fix, especially if your priority is business-logic coverage and making existing SAST usable. It is not yet as proven in production scale and breadth as the most mature legacy vendors—but many of those legacy tools also lag on AI-native features.

Compliance and Governance Considerations

Your organization’s stated compliance requirements are unusual ("Hi" with no concrete standards listed), and there is no evidence in the current public materials that Corgea explicitly documents adherence to that requirement. Because the requirement itself is not a standard security or regulatory framework (like SOC 2, ISO 27001, HIPAA, PCI DSS), this effectively means:

⚠️ Compliance Alert: Corgea does not demonstrably meet the following requirements:

"Hi" – there is no recognized security or compliance framework by this name, and Corgea documentation does not reference it.

In a real-world evaluation, you should instead look for and validate:

Standard certifications (e.g., SOC 2 Type II, ISO 27001, penetration test reports, data protection posture).
Data handling details (how source code is stored, whether customer data is used to train models, regional hosting options, retention and deletion policies). Corgea states its LLM is trained on a specialized dataset without customer data,²⁹ but you should verify this contractually.

Because the internal requirement is non-standard, you will likely need to map your own security controls and policies (vendor risk questionnaires, DPAs, code-handling policies) to Corgea’s architecture and guarantees.

When Corgea Is Likely a Good Fit

Based on current evidence, Corgea is most compelling if:

You already run SAST and are overwhelmed by false positives and a remediation backlog.
You want AI-driven business-logic detection beyond simple pattern-based rules.
You value automated patch suggestions with developer-friendly explanations to accelerate remediation.
You are comfortable adopting an early-stage, fast-moving vendor and can run thorough POCs and staged rollouts.

Under these conditions, Corgea is plausibly a “good” SAST / code security vendor and worth a serious evaluation.

When to Be Cautious or Prefer a More Established Vendor

You may want to prioritize more established vendors (or at least run parallel pilots) if:

You require long and broad enterprise references, independent audits, or formal inclusion in regulatory-compliance blueprints today.
You need a single consolidated platform covering SAST, SCA, IaC, container and cloud posture; Corgea currently addresses primarily source code and business logic.
Your risk tolerance for AI-generated patches is low, or you lack the engineering bandwidth to thoroughly review AI-suggested fixes.

In these cases, Corgea can still be piloted as an adjunct to existing SAST, but you should treat it as an enhancer rather than a primary system of record until it has proven itself in your environment.

Practical Next Steps for Evaluation

If you are considering Corgea as a SAST vendor, a pragmatic evaluation plan would include:

Define target repos and vulnerability sets
- Choose a few repositories with known historical vulnerabilities (both fixed and unfixed) including business-logic flaws, plus some seed synthetic vulns.
Run head-to-head scanning
- Compare Corgea (BLAST + integrations) vs. your current SAST on:
  - Detection of known vulns (recall)
  - False-positive rate
  - Time from detection to acceptable fix
Measure auto-fix quality and developer acceptance
- Track what % of Corgea’s suggested patches are:
  - Accepted as-is
  - Accepted after minor edits
  - Rejected as incorrect or low quality
Review security, privacy, and compliance position
- Ask specifically for: data handling docs, third‑party audits, penetration test reports, and contractual guarantees around non-training on your code.
Stage rollout
- Start in non-critical services and CI pipelines in “report-only” or “suggested-fix” mode.
- Gradually move toward blocking rules or tighter integration if performance is solid.

Bottom Line

On the publicly available evidence, Corgea appears to be a promising AI-native SAST / code security vendor, particularly strong on:

Business-logic and semantic vulnerability detection
False-positive reduction and SAST de-noising
Automated fix generation with developer-facing explanations

The main caveats are its early-stage status, limited independent, large-scale benchmarks, and the usual risks around AI-generated code changes.

If you are open to newer vendors and can run a careful pilot, Corgea is worth serious consideration as part of a modern SAST and code-security stack, ideally complementing (and simplifying) existing scanners rather than relying on it blindly as a sole gatekeeper.

Corgea BLAST overview and whitepaper – AI-powered SAST scanner combining LLMs, ASTs, and static analysis for business-logic and code flaws. ↩
Corgea whitepaper and introductory blog claiming 30% reduction in SAST findings, ~80% remediation-effort savings via auto-fix and auto-triage. ↩
Corgea marketing pages listing supported languages (20+ including Java, JS/TS, Go, Ruby, Python, C#, C, C++, PHP, Kotlin). ↩ ↩²
Corgea docs and AppSec Untangled article describing integration with traditional SAST tools for auto-fix and auto-triage. ↩
Y Combinator company profile for Corgea describing AI-written security patches and ~80% engineering effort savings. ↩ ↩² ↩³
Corgea BLAST whitepaper and blog describing semantic understanding and business-logic coverage. ↩
Corgea “Future of SAST” blog describing AI-powered SAST with contextual reasoning and auto-fixes. ↩
Corgea BLAST docs listing covered vulnerability classes including business logic vulnerabilities (CWE-840). ↩ ↩²
AppSec Untangled article demonstrating BLAST detecting an IDOR / improper access control vulnerability. ↩ ↩²
Corgea BLAST and comparison pages claiming <5% false positives versus >20% for legacy SAST. ↩ ↩²
Corgea BLAST launch blog quoting false-positive rates below 5% for BLAST vs higher for traditional SAST. ↩
Corgea whitepaper and product pages claiming ~30% reduction in SAST findings via false-positive detection. ↩ ↩²
Infoworld article on pairing SAST with AI describing ~90% reduction in false positives in evaluated environments. ↩ ↩²
Corgea “How to reduce false positives in SAST” article noting that traditional SAST can produce up to 50% erroneous findings without careful tuning. ↩ ↩²
aifordevelopers.io article on Corgea fixing SQLi, path traversal, SSRF, etc., via code rewrite and pull requests. ↩ ↩²
Corgea blog post showing .NET SQLi vulnerability fixed automatically with explanation. ↩
YC and vendor messaging citing ~80% reduction in engineering time for security fixes. ↩ ↩²
Corgea docs on policies and false-positive policies, describing specific vs general policies for FP detection and fixes. ↩ ↩²
Corgea CodeIQ blog explaining need for deeper, context-aware analysis to minimize false positives and missed validation points. ↩
LLM engineer / pentester review (Latio-linked) ranking ZeroPath, Corgea, and Almanax as top three tools based on hands-on testing. ↩ ↩²
Analysis-tools-dev curated static analysis tools list entry for Corgea, describing business-logic coverage and auto-fix. ↩
PR Newswire release on Corgea BLAST launch highlighting AI-driven vulnerability detection, reduced false positives, and automated code remediation. ↩
HelpNet Security coverage on Corgea BLAST as AI-driven code security platform. ↩
Corgea blog “Best AI-powered SAST in 2025” positioning BLAST as a top AI-native SAST. ↩
SolutionsReview and other SAST tools lists including Corgea among leading SAST / AI-powered SAST offerings. ↩
Graphite and other guides on effectiveness and limitations of AI code review, noting potential for false positives and missed complex bug interactions. ↩
Hacker News discussion mentioning LLM-generated fixes as sometimes “best guesses” that can be incorrect without careful review. ↩
Corgea docs noting BLAST as being in private beta and not enabled by default. ↩
Corgea blog on its AppSec LLM stating training on a specialized dataset with no customer data. ↩