At Corgea, we’re continually advancing the way security teams and developers handle vulnerabilities. Today, we’re thrilled to introduce Corgea CodeIQ, a breakthrough feature designed to solve a fundamental problem in secure code analysis: gaining a deep understanding of your codebase and how the pieces of code you’re analyzing relate to the broader system.

The Problem We're Solving

To detect vulnerabilities, minimize false positives, and effectively fix issues, a tool needs more than just surface-level access to code. It must have a full contextual understanding of the codebase—this includes files, configurations, and interdependencies that aren’t always obvious. Traditional static analysis tools often fall short because they can’t "see" the broader picture of how different pieces of code work together. Corgea CodeIQ solves this by intelligently pulling in all the relevant files and context needed to accurately detect, triage, and fix vulnerabilities.

A Brief History of Static Analysis

Static analysis has been a cornerstone of code security for decades, relying on techniques like Code Property Graphs (CPGs) and call-graphs to map relationships between code elements. These methods focus on tracking explicit function calls and dependencies but often miss critical details, like source-sink analysis—the flow of data from untrusted inputs (sources) to potentially vulnerable parts of the code (sinks). More recently, vector search and Retrieval-Augmented Generation (RAG) have emerged, offering new ways to semantically search and generate insights from the codebase. However, these techniques come with their own limitations.

The Problem with Traditional Methods

Both source-sink analysis and call-graphs are powerful techniques, but they have significant disadvantages:

• Source-Sink Analysis often leads to false positives, especially when it misses critical validation steps. It also struggles with context sensitivity and framework-specific functionality, such as middleware processes in web frameworks. Additionally, path explosion in large codebases can make the analysis inefficient. More critically, source-sink analysis lacks the ability to handle implicit data flows and semantic meaning, leading to incomplete assessments.

• Call Graphs suffer from namespace conflicts and lack of context, missing dynamic behaviors like reflection or implicit framework calls. These graphs can become too complex to offer actionable insights and often fail to capture runtime-specific execution paths.

Consider a Django application: Middleware defined in a settings file may not show up in a traditional call-graph but is essential to how the application behaves during runtime. This gap leaves potential vulnerabilities undetected. Corgea CodeIQ addresses this by pulling in the broader context—whether it’s middleware, configuration files, or even documentation—ensuring nothing crucial is overlooked.

The Problem with New Methods: Vector Search and RAG

While vector search and Retrieval-Augmented Generation (RAG) techniques bring new capabilities to static code analysis, they also introduce their own set of limitations:

• Vector Search relies on semantic embeddings to find similar pieces of code, but this often results in over-generalized and irrelevant results. It may retrieve files like migration scripts or tests that have no bearing on the security issue being analyzed, leading to high noise levels. Additionally, vector search lacks the precision needed to handle exact source code context, making it less effective for detecting vulnerabilities that require detailed understanding of control flow or data flow.

  Moreover, vector search doesn’t provide deterministic guarantees, which can be critical in security analysis where missing a single vulnerability could have severe consequences. It also struggles with dynamic, runtime-specific code, such as dependency injection or dynamically generated functions, and incurs performance issues when embedding and re-indexing large, evolving codebases.

• RAG-based techniques further compound these issues. While they combine retrieval with generation to produce insightful responses, RAG models heavily depend on the quality of the documents retrieved. If the retrieved snippets are incomplete or irrelevant, the generation process produces inconsistent or inaccurate results. Additionally, RAG models can suffer from hallucination problems, generating plausible-sounding but incorrect code, which is especially dangerous in security contexts.

  RAG also struggles to handle complex logical relationships in code, particularly in domain-specific or framework-heavy environments. The computational costs of running both retrieval and generation make RAG less suited for real-time analysis in fast-moving projects.

The Power of Corgea CodeIQ: AI Meets ASTs

Corgea CodeIQ fundamentally changes how we approach secure code analysis by leveraging Artificial Intelligence (AI), Abstract Syntax Trees (ASTs), and project analysis. Here’s how it works:

1. Project Parsing: CodeIQ analyzes the entire project, and uses ASTs to break down the structure of the code to understand how different components are related.

2. AI Contextual Understanding: The AI then steps in to provide contextual insights, which is where the LLM excels. It understands complex code logic, implicit connections, and framework-specific nuances that traditional methods often miss.

3. Adaptability: CodeIQ adapts to the specific codebase it’s operating in, pulling in just the right amount of context—whether it’s middleware, configurations, or templates—ensuring accurate detection without overwhelming you with irrelevant data.

This combination of project analysis, AI, and ASTs allows CodeIQ to offer both precision and contextual intelligence, going beyond what traditional static analysis can achieve.

Moving Beyond Traditional Methods

Unlike CPGs and call-graphs, which are prone to namespace conflicts and miss implicit dependencies, CodeIQ excels at identifying the broader security context. For instance, a login function may have its authentication mechanism implemented elsewhere in the code—possibly in middleware or a settings file. Traditional tools can miss this, but CodeIQ pulls in these critical pieces to ensure comprehensive security coverage.

Additionally, vector searches often bring in irrelevant data due to their semantic nature, such as migration and test files that don’t contribute to real security analysis. RAG models, while capable of generating insightful responses, depend heavily on the quality of retrieved data, and can suffer from inconsistent outputs and hallucinations. CodeIQ filters out this noise and adapts to your specific codebase, allowing you to focus on what matters most—actual vulnerabilities that need fixing.

What’s Already Here: BLAST and What’s Coming Next

We’re excited to announce that CodeIQ is already powering BLAST, our internal system for comprehensive vulnerability detection. And soon, CodeIQ will enhance false positive detection and auto-fixing, bringing even more value to your security process.

But we’re not stopping there. We’re also working on new and advanced triaging capabilities, which will analyze vulnerabilities just like a software developer would. This upcoming triaging feature will help teams prioritize their response, focusing on the highest-risk issues first.

A Glimpse Into the Future

CodeIQ is just the beginning. Soon, you’ll see features like multi-file fixes, where Corgea can fix vulnerabilities across multiple parts of the codebase simultaneously, making it faster and easier to secure large, complex projects. This will make Corgea an even more powerful tool in your security toolkit.

With Corgea CodeIQ, you’re not just detecting vulnerabilities—you’re gaining a deep, contextual understanding of your codebase. And this is only the beginning. Stay tuned for even more innovations as we continue to push the boundaries of secure code analysis.