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@@ -425,7 +425,7 @@ In addition to traditional security problems associated with software, LLMs have
 
 * **Prompt hacking**: Different techniques related to prompt engineering, including prompt injection (additional instruction to hijack the model's answer), data/prompt leaking (retrieve its original data/prompt), and jailbreaking (craft prompts to bypass safety features).
 * **Backdoors**: Attack vectors can target the training data itself, by poisoning the training data (e.g., with false information) or creating backdoors (secret triggers to change the model's behavior during inference).
-* **Defensive measures**: The best way to protect your LLM applications is to test them against these vulnerabilities (e.g., using red teaming and checks like [garak](https://github.com/leondz/garak/)) and observe them in production (with a framework like [langfuse](https://github.com/langfuse/langfuse)).
+* **Defensive measures**: The best way to protect your LLM applications is to test them against these vulnerabilities (e.g., using red teaming and checks like [garak](https://github.com/leondz/garak/)) and observe them in production (with a framework like [langfuse](https://github.com/langfuse/langfuse) or [agenta](https://github.com/agenta-ai/agenta)).
 
 📚 **References**:
 * [OWASP LLM Top 10](https://owasp.org/www-project-top-10-for-large-language-model-applications/) by HEGO Wiki: List of the 10 most critical vulnerabilities seen in LLM applications.