Semgrep's Multimodal Tackles AI Code Security With Hybrid Analysis

SAN FRANCISCO, CA – March 19, 2026

Code security firm Semgrep today announced the launch of Semgrep Multimodal, a new system designed to secure software in an era increasingly dominated by AI-generated code. The platform combines the deterministic precision of rule-based scanning with the contextual reasoning of artificial intelligence, aiming to find significantly more critical vulnerabilities while drastically reducing the noise that plagues security teams. The company claims the new system can find up to eight times more true positives and cut false positive noise by 50% compared to using foundation AI models alone, and has already been credited with discovering dozens of zero-day vulnerabilities at customer sites.

A Hybrid Answer to an AI-Scale Problem

The rapid adoption of AI coding assistants has created a security paradox. While developer productivity has soared, the volume of code being generated has outpaced the capacity of security teams to review it. This deluge of code, often created without a deep understanding of its security implications, creates a massive and growing attack surface. Security teams using traditional tools find themselves caught between two inadequate options.

On one side, traditional Static Application Security Testing (SAST) tools excel at identifying known vulnerability patterns like SQL injection or cross-site scripting (XSS). They are fast and reliable for what they are programmed to find, but they possess a critical blind spot: business logic flaws. These vulnerabilities, such as Insecure Direct Object References (IDORs) or broken authorization, arise from the unique logic of an application and require an understanding of developer intent and context, something rule-based scanners inherently lack.

On the other side, Large Language Models (LLMs) have shown promise in their ability to reason about code and understand context. However, when applied to security scanning in isolation, they often prove unreliable in a production environment. Demos that look impressive in a controlled setting can fall apart at scale, producing inconsistent results, generating costly "hallucinations," and burying security teams in a mountain of false positives that erodes trust in the technology.

Semgrep Multimodal is engineered to bridge this divide. By integrating its well-regarded Semgrep Pro analysis engine with advanced LLM reasoning, it creates a hybrid system that leverages the strengths of both approaches. The rule-based engine provides a solid, deterministic foundation, while the AI layer adds the contextual intelligence needed to uncover more complex and subtle flaws.

Beyond Pattern Matching: Uncovering Logic Flaws and Zero-Days

The true test of a modern security tool is its ability to find the threats that others miss. Semgrep is positioning Multimodal as a solution for precisely this challenge, focusing on the complex business logic vulnerabilities that are often the root cause of the most damaging data breaches. These are the flaws that frequently evade automated scanners and even manual code reviews.

The system's effectiveness stems from its methodical approach. For example, in hunting for IDOR vulnerabilities—where an attacker can access data they shouldn't by manipulating an identifier—Multimodal doesn't simply ask an LLM to "find bugs." Instead, it uses the Semgrep Pro engine to perform a structured analysis, first enumerating all of an application's API routes. It then passes this structured data to the AI model, which analyzes the associated code handlers to determine if proper authorization checks are in place. Endpoints that lack these critical safeguards are flagged as high-confidence potential vulnerabilities.

This grounded, multi-step process significantly improves accuracy. Internal research from Semgrep's security team found that this hybrid detection method achieved a 61% precision rate for identifying IDORs. This was nearly three times more effective than using a standalone LLM, which produced an 88% false positive rate for the same task. This leap in precision is what enables the platform's claim of discovering "dozens of zero-days" in customer environments, identifying previously unknown and potentially critical security gaps before they can be exploited.

Furthermore, the system is designed to learn. Semgrep's platform incorporates a "Memories" feature that learns from the triage decisions made by security engineers and developers. When a finding is marked as a false positive, the system remembers that context, automatically suppressing similar non-issues in the future and continuously refining its accuracy based on organization-specific codebases and practices.

The Engine Room: Customizable Workflows for Autonomous Security

Underpinning the new Multimodal detection capabilities is Semgrep Workflows, a powerful framework that allows security teams to move beyond out-of-the-box scanning and build their own automated security programs. This platform is designed to empower the security engineers closest to the code to define, automate, and scale their organization's unique security policies.

Instead of being a rigid black box, Workflows provides a flexible, code-first environment. Security and development teams can write custom workflows in plain Python, a language familiar to most engineers, to automate a wide range of tasks including detection, triage, remediation guidance, and compliance reporting. For teams that need to get started quickly, Semgrep provides a library of pre-built workflows that cover common vulnerability categories, including the OWASP Top 10 and various business logic flaws.

The goal is to enable a state of autonomous code security, where security processes are encoded once and then reliably scaled across all teams and repositories. By handling the deployment and maintenance of the underlying infrastructure, Semgrep allows teams to focus their energy on defining high-impact security logic rather than managing a complex toolchain. This approach represents a significant shift from reactive vulnerability patching to a proactive, automated system of security governance. Custom Workflows are currently available to early partners in a private beta.

Navigating a Crowded Field in the AI Security Gold Rush

Semgrep's announcement arrives just ahead of the RSA Conference 2026, where the role of artificial intelligence in cybersecurity is expected to be a dominant theme. The industry is in the midst of an AI gold rush, with established giants and nimble startups alike racing to integrate AI into their security offerings. Competitors like Veracode, Checkmarx, and Snyk are all promoting their own AI-powered features, from automated remediation to enhanced threat detection.

Within this competitive landscape, Semgrep is differentiating itself not by simply adding an AI layer, but by deeply integrating AI with its proven program analysis engine. This hybrid model is its core strategic bet—that the key to unlocking AI's potential in code security lies in grounding its reasoning with deterministic, structural analysis to ensure accuracy and consistency at scale.

The timing aligns with a broader industry-wide conversation. Major technology players like Google and IBM are slated to lead discussions at RSA on securing the AI lifecycle and defending against AI-driven threats, validating the critical importance of the problem Semgrep aims to solve. The challenge of securing a software supply chain increasingly built with and by AI is a top priority for CISOs and technology leaders globally.

"Semgrep's rule-based engine became the most widely deployed code scanner in the world by giving teams a way to encode their own security knowledge into precise, customizable rules," said Isaac Evans, CEO and Co-Founder at Semgrep, in the announcement. "Semgrep Multimodal and Workflows are the next chapter of that same bet - that the teams closest to the code are best positioned to define what security means for their organization, and that our job is to give them the engine to automate it."

This philosophy underscores the company's developer-first approach, aiming to embed security as a seamless, intelligent, and scalable part of the development process rather than a cumbersome gatekeeper. As organizations continue to grapple with the dual-edged sword of AI-accelerated development, solutions that can intelligently manage the associated risks will become increasingly indispensable.