Best LLM Security Tools in 2026
Every real LLM deployment hits the same three problems: prompt injection, data leakage, and runtime abuse. The LLM security tooling market now has mature answers for each. Here are the 10 best LLM security tools in 2026, mapped to the OWASP LLM Top 10 and sorted by use case.
The 10 Best LLM Security Tools
- TigerGate AI Security — runtime protection for custom LLM applications, prompt-injection detection, model-input/output monitoring via eBPF, and AI secrets scanning. See AI security and AI-SPM.
- Lakera Guard — prompt injection and jailbreak defense via policy-as-code.
- Protect AI — ML model scanning (joblib, pickle, safetensors) and MLSecOps posture.
- Robust Intelligence — AI firewall, red-teaming, and continuous testing.
- HiddenLayer — model theft detection and adversarial input monitoring.
- CalypsoAI — enterprise LLM gateway with policy enforcement.
- Prompt Security — prompt-level protection as a sidecar.
- Credal — AI gateway + data access controls.
- WhyLabs LangKit — LLM observability and drift monitoring.
- NeMo Guardrails (NVIDIA) — open-source topic / safety / jailbreak guardrails.
OWASP LLM Top 10 Mapping
| OWASP LLM Risk | Tool category |
|---|---|
| LLM01: Prompt Injection | Lakera, Prompt Security, NeMo Guardrails |
| LLM02: Insecure Output Handling | TigerGate, Credal |
| LLM03: Training Data Poisoning | Protect AI, Robust Intelligence |
| LLM04: Model DoS | LLM gateways (CalypsoAI, Credal) |
| LLM05: Supply Chain Vulnerabilities | Protect AI, HiddenLayer |
| LLM06: Sensitive Info Disclosure | TigerGate (AI secrets detection), Credal |
| LLM07: Insecure Plugin Design | LLM gateways + policy |
| LLM08: Excessive Agency | Gateways + action allowlists |
| LLM09: Overreliance | Observability (WhyLabs) |
| LLM10: Model Theft | HiddenLayer, TigerGate |
Runtime Protection for Custom LLM Applications
If you're building on OpenAI, Anthropic, or self-hosted models with LangChain / LlamaIndex / your own orchestration, the attack surface sits in your application code — prompt handling, tool calls, memory, and output rendering. Runtime protection means observing what the LLM actually did at request time: which tools were invoked, what data was returned, whether a tool argument looks like a jailbreak. TigerGate's eBPF agent correlates LLM calls with the surrounding workload behavior for this.
AI Secrets Detection
The newest LLM-specific risk is secret leakage from prompts and training data. AI secrets detection tools scan prompts, embeddings, and completions for credentials, PII, and sensitive strings. TigerGate's secrets scanner extends to LLM input/output pipelines in production.
FAQ
What is the best LLM security software?
For a dedicated prompt firewall, Lakera or Prompt Security. For end-to-end AI + application + runtime, TigerGate. The right answer depends on whether you want a focused tool or a platform.
Do I need an LLM security tool if I use OpenAI's moderation API?
Yes. Moderation covers content safety, not prompt injection, data leakage, tool abuse, or supply chain. Those require application-level and runtime controls.
How do I compare real-time LLM security providers?
Evaluate on three axes: coverage of the OWASP LLM Top 10, latency added per request, and integration model (SDK, proxy, sidecar, or eBPF). See the table above.
AI Security + Runtime in One Platform
TigerGate covers prompt injection, AI secrets detection, model scanning, and LLM runtime protection — alongside the rest of your cloud security stack.
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