Best AI Security Software for TypeScript

ZeroPath (YC S24) is an AI-native application security platform that delivers comprehensive code protection beyond traditional SAST. Founded by security engineers from Tesla and Google, ZeroPath combines large language models with deep program analysis to deliver intelligent security testing that finds real vulnerabilities while dramatically reducing false positives. Unlike traditional SAST tools that rely on pattern matching, ZeroPath understands code context, business logic, and developer intent. This enables identification of sophisticated security issues including business logic flaws, broken authentication, authorization bypasses, and complex dependency vulnerabilities. Our comprehensive security suite covers the application security lifecycle: 1. AI-powered SAST 2. Software Composition Analysis with reachability analysis 3. Secrets detection and validation 4. Infrastructure as Code scanning 5. Automated PR reviews 6. Automated patch generation and more... ZeroPath integrates seamlessly with GitHub, GitLab, Bitbucket, Azure DevOps and many more. The platform handles codebases with millions of lines across Python, JavaScript, TypeScript, Java, Go, Ruby, Rust, PHP, Kotlin and more. Our research team has been successful in finding vulnerabilities like critical account takeover in better-auth (CVE-2025-61928, 300k+ weekly downloads), identifying 170+ verified bugs in curl, and discovering 0-days in production systems at Netflix, Hulu, and Salesforce. Trusted by 750+ companies and performing 200k+ code scans monthly.

Backslash Security is the governance and visibility platform built for organizations where AI coding tools are already part of how software gets built. GitHub Copilot, Cursor, Windsurf, Claude Code, and Gemini CLI have fundamentally changed the development lifecycle — and the security controls most organizations rely on were not designed for this environment. Backslash provides a comprehensive AI coding tool inventory and policy enforcement across the full AI coding spectrum, giving security teams visibility into every active tool and the risk introduced before it reaches production. This includes vibe coding security — risk detection purpose-built for vulnerability patterns in AI-generated code that traditional scanners are not equipped to catch. As AI coding agents grow more capable, they increasingly operate with access to external services, internal data, and organizational infrastructure through MCP servers. Over-permissioned agents and misconfigured MCP connections create data leakage pathways — exposing sensitive organizational data to AI models without security team awareness or enforcement controls. These are active exposure points, not theoretical risks. Backslash addresses this directly. The platform maps every MCP server connection, identifies over-permissioned AI agent configurations, and enforces least-privilege access before data leakage occurs. Security teams gain full visibility into what AI agents can access and where permissions exceed what the task requires. For security leaders governing an environment that moved faster than their controls, Backslash is the missing layer — built from the ground up for AI-native development, not retrofitted from a previous generation of tooling.

Silmaril is an innovative defense mechanism against prompt injection that autonomously heals itself, aiming to safeguard AI systems from sophisticated, multi-layered threats that conventional barriers cannot mitigate. Unlike traditional methods that merely filter inputs, it envelops inference calls, assessing whether the sequence of actions is steering towards a detrimental result. By employing a multihead classifier, it evaluates user intentions, application contexts, and execution states simultaneously, which allows it to identify indirect injections, multi-turn attack sequences, context manipulation, and tool exploitation before any harm can occur. To enhance its protective capabilities, Silmaril incorporates autonomous threat-hunting agents that explore systems, identify weaknesses, and produce synthetic training data based on actual attack incidents. These findings facilitate automatic model retraining, allowing for the deployment of updated defenses in less than an hour, while simultaneously disseminating anonymized protective measures across all instances. Moreover, this proactive approach ensures that the system remains resilient against emerging threats, adapting continuously to the evolving landscape of cybersecurity challenges.