Top LLM security tools to protect AI applications

Aikido Security's State of AI in Security and Development 2026 report finds that 24% of production code is now AI-generated, but 69% of organizations have discovered vulnerabilities in AI-generated code and 20% reported a serious incident as a result. The report highlights that application-layer security, including SAST, SCA, and supply chain malware detection, is critical for protecting LLM-based applications, as demonstrated by a January 2025 misconfiguration in Lovable-built apps that exposed sensitive data and by Aikido's PromptPwnd research showing untrusted input injection into Gemini CLI agents affecting Fortune 500 companies.

AI is now writing a large share of the software in production. According to Aikido's State of AI in Security and Development 2026 /reports/2026-state-of-ai-in-security-development report, 24% of production code is now AI-generated. However, 69% of organizations have discovered vulnerabilities in AI-generated code and 20% reported a serious incident as a result. A lot of the security conversation around LLMs focuses on putting guardrails on the models. But far less attention goes to the application layer, even though that's where many of these incidents originate, and where guardrails can't help. In January 2025, a critical misconfiguration in AI-generated code https://vibeappscanner.com/lovable-vulnerability-cve-2025-48757 left over 170 Lovable-built apps exposing emails, API keys, payment details, and personal data. No guardrail sitting in front of a model would have caught it. This post walks through what LLM application security actually covers, why you need it how to choose a tool that addresses it, and which platforms offer the best coverage {{cta}} TL;DR Aikido Security is the strongest option for teams that want enterprise-grade LLM application security across the whole AI development lifecycle. It scans AI-generated code in the IDE, blocks malicious and hallucinated packages at install time, protects the developer environment against threats like malicious MCP servers, and tracks which AI models your application is calling in production. What is LLM application security? In this piece, we will focus on LLM security from the application side, which means securing the code, dependencies, and infrastructure around an LLM. It's distinct from model security, which covers the LLM's behavior at runtime, and is handled by guardrails and red-teaming tools like Lakera Guard, Prisma AIRS, NeMo Guardrails, and Garak. Neither is sufficient alone. Aikido's PromptPwnd /blog/promptpwnd-github-actions-ai-agents research showed why the application side deserves attention. Untrusted input got injected into prompts for Gemini CLI and other agents running inside GitHub Actions workflows, and those agents used their privileged tokens to leak secrets. At least five Fortune 500 companies were affected. The model was the point of entry for the attacker, but the vulnerability lived in the workflow file, which is something application security tools can check. SAST rules can flag untrusted input flowing into prompts and privileged tokens exposed to agents, which is exactly what Aikido's Opengrep rules detect and what Google fixed within four days of disclosure. The OWASP Top 10 for LLM Applications 2025 https://genai.owasp.org/llm-top-10/ is the reference framework for this space, and several of its categories land on the application layer, including Supply Chain LLM03 , Improper output handling LLM05 , Excessive agency LLM06 , and Misinformation LLM09 . But the taxonomy was written with runtime LLM applications in mind, and it only partially captures AI-assisted development risks. These vulnerabilities are already appearing in the real world. Under LLM09, Researchers at USENIX 2025 https://labs.cloudsecurityalliance.org/wp-content/uploads/2026/04/CSA research note slopsquatting-ai-supply-chain 20260419-csa-styled-1.pdf tested 16 models across 2.23 million code samples and found that 19.7% contained at least one hallucinated package name. When identical prompts were re-run, 43% of hallucinated packages reappeared in every one of 10 queries. Attackers now register fake names in advance, turning model quirks into supply chain attacks. Covering these categories takes several distinct capabilities. SAST and secret scanning on application code catch hardcoded credentials and unsanitized inputs flowing into prompts. SCA flags known-vulnerable AI framework versions, while supply chain malware detection catches malicious and hallucinated packages. Developer environment protection catches malicious MCP servers distributed as packages and compromised extensions before they're installed. And LLM usage tracking watches which models your application is actually calling, surfacing unauthorized AI integrations. Why you need LLM security tools AI development has introduced new vulnerabilities that security teams need to address. AI-generated code ships unreviewed: With a quarter of production code now AI-generated, review hasn't kept pace with output. LLMs inherit the vulnerabilities of the code they run in: It's not just the AI-written code that matters. Any code an LLM reads, executes against, or takes context from becomes part of its attack surface, which is exactly what made PromptPwnd exploitable. Slopsquatted packages: Attackers register the package names AI assistants hallucinate, turning a model quirk into a supply chain attack. Shadow AI : Teams are calling models nobody inventoried, sending data to providers nobody approved. Compliance pressure is starting to ask for LLM security: The EU AI Act https://artificialintelligenceact.eu/ is the clearest example, and the OWASP LLM Top 10 is becoming the de facto reference framework in security reviews. How to choose an LLM security tool Coverage of the whole AI SDLC A platform that covers AI-generated code, the supply chain feeding it, and the developer environment producing it. Point tools cover a slice, and stitching vendors together means multiple dashboards and duplicate findings. Does it check AI generated code in the IDE? Code written by an assistant should be scanned at the point of creation, not discovered in CI after it's already in a pull request. The IDE is the first line of defense against vulnerabilities in AI generated code, because it's where a human is actively looking at every line. Signal-to-noise ratio AI has inflated the volume of code and findings alike. A tool that can't triage its own output turns into another backlog. LLM usage tracking Visibility into which models the application calls, so shadow AI surfaces before an auditor or an attacker finds it first. Top LLM security tools for AI applications Aikido Security Aikido Security / covers the AI application lifecycle end-to-end. That starts in the IDE /code/ide-integrations , where AI-generated code originates, and runs through to the production environment where it executes. At the code layer, Aikido's MCP plugin https://help.aikido.dev/ai-and-dev-tools/aikido-mcp connects Aikido's security engine directly to AI coding tools, automatically running SAST /code/static-code-analysis-sast and secrets detection /code/secrets-detection on generated code inside the IDE. Vulnerabilities get caught at the point of creation instead of surfacing in a pull request or, worse, in production. Safe Chain /protect/safe-chain sits alongside it, blocking malicious and hallucinated npm packages at install time and cutting off the slopsquatting /blog/slopsquatting-ai-package-hallucination-attacks attack path before a package ever lands in your dependency tree a direct match for OWASP LLM09 . At the environment layer, Device Protection /protect/device-protection watches the machines where AI-assisted development happens. AI coding tools connect to MCP servers, install extensions, and pull packages, and each of those is a channel an attacker can exploit. Device protection catches malicious MCP servers and compromised extensions before they're installed. DSPM /cloud/dspm addresses where AI application data ends up. Sensitive customer data flows into stores that traditional tools don't see, including vector databases and prompt logs, often without being redacted first. DSPM finds that data sitting exposed, so PII doesn't quietly accumulate in the infrastructure your AI features write to. And in production, Zen /protect/zen provides in-app LLM usage tracking that shows exactly which AI models your application is calling in real time, tracks where data is going down to the region level, and enforces AI usage compliance so unauthorized integrations surface immediately. Behind all of this, AutoTriage /blog-tags/autotriage does the deduplication, reachability filtering, and cross-scanner correlation that keeps AI-inflated finding volumes manageable at enterprise scale. And RBAC, SSO, and audit trails sit behind every layer, so enterprise governance requirements are met. Best for: Enterprise teams that want AI application security across the whole development lifecycle, with the RBAC, SSO, and audit trails governance requires. {{walkthrough}} Snyk Snyk's platform spans SAST, SCA, container, and IaC scanning, with its DeepCode AI engine powering detection and AI-generated code analysis in the IDE. Over the past year, Snyk has repositioned itself as an "AI Security Platform," rolling out Evo AI-SPM for agentic AI posture management, Agent Security for governing AI agents across the lifecycle, and Agent Fix for autonomous remediation in the IDE. The bankable value is still Snyk's traditional AppSec engine, as most of these AI products are less than a year old, so they haven't been battle-tested at the scale of Snyk's traditional SAST and SCA engines yet. The usual tradeoffs with Snyk are also unchanged. Snyk's finding volume is a common complaint, and its pricing model works better for large organizations than for small teams. Best for : teams that want an established platform with broad language coverage and are willing to invest in Snyk's expanding AI posture tooling. But teams should budget for tuning time to keep the finding volume manageable, and expect enterprise pricing that won't fit smaller organizations. Semgrep Semgrep's strength is customizable rules. Teams can write their own detection logic for LLM-specific code patterns. Semgrep Multimodal, released in March 2026, pairs Semgrep's deterministic rule engine with LLM reasoning to reduce triage load and generate step-by-step remediation guidance in pull requests. Semgrep Guardian, launched in May 2026, is real-time security scanning for AI-generated code that runs inside Claude Code, Cursor, Windsurf, Kiro, and other agentic coding tools. It ships with three curated rule packs specifically aimed at AI risks. The tradeoff is scope. Semgrep is a code security platform, so supply chain protection beyond package vulnerability scanning, developer environment defense, DSPM, and production LLM usage tracking all need to come from somewhere else. The rule-based foundation is also still a factor. Coverage depends on which rule packs you enable and how you tune them, though the AI-specific packs Semgrep ships now do a lot more of that work out of the box than they did in the past. Best for: Security engineering teams that want to write and tune their own detection rules, and are comfortable with a code-focused platform rather than a full-stack security suite. But you'll still need separate coverage for supply chain protection, developer environment threats, and production LLM usage visibility. Endor Labs Endor Labs is a unified platform covering SCA, SAST, secrets detection, container scanning, and malicious package detection through its Package Firewall. On the AI side, Endor discovers AI models pulled into your codebase, generates AI-BOMs, and offers risk scoring for models from public repositories like Hugging Face. Their AURI MCP server plugs directly into Cursor, Claude Code, Copilot, and other AI coding assistants for real-time scanning as code gets written. But Endor doesn't cover the developer environment beyond IDE plugins no protection against malicious MCP servers as installed threats , doesn't run in production no LLM usage tracking, no runtime application protection , and doesn't do DSPM. Best for: teams whose primary AI risk is the open source supply chain, but if your risks include AI-generated code in the IDE, malicious MCP servers on developer machines, or unauthorized model calls in production, you'll need something else alongside it. Wiz Wiz approaches AI security from the cloud. Its AI-SPM discovers AI services, models, and training infrastructure across cloud environments and flags misconfigurations and exposure. Wiz DSPM extends that discovery into vector stores and prompt logs where sensitive AI-adjacent data tends to accumulate. Wiz Code, launched to expand into the developer workflow, covers SAST, SCA, secrets detection, and IaC scanning with IDE plugins for VS Code, JetBrains, and Lovable. Since the Google acquisition closed in March 2026, Wiz continues to operate under its own brand within Google Cloud. Where Wiz's coverage thins out is at the earliest points of the AI development pipeline. There's no install-time blocking for malicious packages Wiz identifies them after the fact rather than intercepting before install , no protection for the developer environment against malicious MCP servers, and no request-level LLM usage tracking inside the application. Best for: security teams whose AI security question is cloud-wide visibility, but if your goal is stopping AI-related vulnerabilities before they reach production blocking malicious packages before install, catching MCP server threats on developer machines, tracking model calls in production , Wiz's strengths sit further downstream than you'll want.