u/Background-Song2007

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

• Which closed-source models would you recommend for generating high-quality attacks?
• Which open-source models have worked well for you?
• Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

Hi everyone,

I'm working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We're currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I'm struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I'm already considering:

\- Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

W advice, resources, or lessons learned from production deployments would be greatly appreciated.

Thank you!

Hi everyone,

I'm working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We're currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I'm struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I'm already considering:

\- Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

W advice, resources, or lessons learned from production deployments would be greatly appreciated.

Thank you!

Hi everyone,

I'm working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We're currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I'm struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I'm already considering:

\- Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

W advice, resources, or lessons learned from production deployments would be greatly appreciated.

Thank you!

Hi everyone,

I'm working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We're currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I'm struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I'm already considering:

Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

W advice, resources, or lessons learned from production deployments would be greatly appreciated.

Thank you!

Hi everyone,

I’m working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We’re currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I’m struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I’m already considering: Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

W advice, resources, or lessons learned from production deployments would be greatly appreciated.

Hi everyone,

I’m working on an agentic AI system that connects to enterprise databases and knowledge sources using a combination of text-to-SQL, SQL execution, RAG, and tool-calling agents.

We’re currently evaluating whether our PoC is ready to evolve into an MVP/production solution. While performance metrics are relatively straightforward to measure, I’m struggling with the security assessment.

What security tests and evaluation metrics would you recommend for such a system?

I’m already considering: Prompt injection

How do you determine whether an agentic AI system is secure enough for production? Are there any frameworks, benchmarks, red-teaming methodologies, or mandatory security layers that you would recommend?

Any advice, resources, or lessons learned from production deployments would be greatly appreciated.

Thank you!