AI is now writing a large share of the software in production. According to Aikido's State of AI in Security and Development 2026 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 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.
The best LLM security tools in 2026 secure the application around the model, not only the model itself. For AI application security across code, the supply chain, the developer environment, and runtime, Aikido Security offers the widest coverage, with Snyk, Semgrep, Endor Labs, and Wiz as strong options. Model-layer guardrails like Lakera and Prisma AIRS cover runtime model behavior, and most teams need both halves.
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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. The line is not absolute: pentesting the deployed application and its agents, as Aikido's AI Pentest does against the OWASP Top 10 for LLM and agentic applications, tests runtime behavior like prompt injection from the application side too. No single layer is sufficient alone.
Aikido's PromptPwnd 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 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 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 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, where AI-generated code originates, and runs through to the production environment where it executes.
At the code layer, Aikido's MCP plugin connects Aikido's security engine directly to AI coding tools, automatically running SAST and 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 sits alongside it, blocking malicious and hallucinated npm packages at install time and cutting off the slopsquatting attack path before a package ever lands in your dependency tree (a direct match for OWASP LLM09).
At the environment layer, 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 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 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.
Aikido also pentests the running AI application itself. Its AI Pentest runs an assessment mapped to the OWASP Top 10 for LLM and agentic applications, testing for prompt injection (direct and indirect), sensitive information disclosure, excessive agency, insecure tool use, insecure output handling, and memory poisoning, and it can test MCP servers directly. You can scope an assessment to AI capabilities only, or run it alongside standard pentest coverage like IDOR, broken access control, SSRF, and business logic abuse, which matters because agentic systems often turn a classic web flaw into a higher-impact exploit chain. Findings are validated against the live application, with AutoFix and one-click retest, and the results give teams a concrete input for EU AI Act readiness.
Behind all of this, 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.
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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.
Model security and guardrail tools
The tools above secure the application around the model. The other half of LLM security guards the model's behavior at runtime, through input and output guardrails and red-teaming. Most teams need both, since a guardrail in front of the model cannot fix an insecure workflow file, and application security cannot stop a jailbreak at inference time. This layer is consolidating fast, with several leaders now inside larger platforms, alongside strong open-source options.
Others in this space include Giskard and Mindgard for AI testing and red-teaming, and Zenity for governing AI agents and copilots. Aikido overlaps this layer from the application side, its AI Pentest tests the deployed app and its agents for OWASP LLM Top 10 risks like prompt injection, while the tools here focus on blocking and monitoring model behavior in production. The two pair rather than replace each other.
