Semgrep Targets LLM-Driven Code Security With Launch of Multimodal Product

According to a recent LinkedIn post from Semgrep, the company is positioning its next-generation offering around autonomous agents rather than human-centric workflows in code scanning. The post describes ongoing work to decompose Semgrep’s technology into smaller program analysis components that large language models can query to perform security analysis.

The LinkedIn post highlights a new product, Semgrep Multimodal, which was introduced during a recent keynote. According to the post, internal benchmarks suggest the tool delivers eight times more true positives and 50% fewer false positives compared with using a base foundation model alone.

The post also notes that many buyers at RSA appear focused on “check-the-box” compliance, with access to free open-source Semgrep, platform-native tools such as GitHub Advanced Security, and foundation models. This framing implies that Semgrep is seeking to differentiate on accuracy and agent-focused capabilities rather than solely on cost or basic feature parity.

For investors, this emphasis on multimodal, LLM-enabled program analysis may signal a move up the value chain in application security tooling. If Semgrep Multimodal’s claimed detection and precision advantages prove durable, the product could strengthen the company’s competitive position against platform-integrated and open-source alternatives.

The post’s focus on demos, talks, and workshops at RSA suggests the company is using the conference as a key channel for demand generation and enterprise engagement. Successful conversion of these high-intent prospects could translate into higher average contract values and deeper integration into customers’ development and security workflows over time.