If you are building with frameworks like LangGraph, CrewAI, or wiring up your own custom loops, you already know the reality. The leap from a simple conversational LLM to an autonomous agent with tool access completely changes your attack surface. It is no longer just about preventing a chatbot from saying something embarrassing. It is about stopping an agent from autonomously dropping a database or maxing out your AWS bill.

We spend a lot of time testing and breaking these systems at Lares. My colleague Raúl Redondo, u/Raul_RT, our Senior Adversarial Engineer, recently published a comprehensive breakdown of the OWASP Top 10 specifically tailored for LLM Agents.

We've been getting a lot of good feedback on this, so I wanted to bring the core of that research directly to this community so y'all have a standalone checklist for your own builds. Here are some of the top critical vulnerabilities from the framework that you need to account for before hitting production:

1. Overprivileged Tool Access

Giving an agent generic "Full Access" to a database or API is the quickest way to a compromise. Agents must operate on the principle of least privilege. If your worker agent only needs to read a table to summarize data, do not give its database tool write permissions.

2. Recursive Loop Exhaustion

This is a failure mode entirely unique to autonomy. A malicious input or a simple logic error can trap an agent in an endless loop of tool calls. Without hard limits on execution time or maximum iterations, this will silently drain your API credits and compute resources.

3. Persona and System Prompt Hijacking

Attackers are no longer just injecting prompts. They are actively forcing the agent to abandon its core system instructions. Once the persona is hijacked, the attacker essentially gains control over the agent's assigned tools and downstream actions.

4. Unverified Tool Inputs (Blind Trust)

Never trust the output of an LLM directly into an execution environment. If your agent drafts a SQL query or a terminal command, that output must be strictly sanitized and validated before the tool actually executes it.

5. Context Window Poisoning

If your agent uses RAG to pull in outside information, an attacker can plant malicious instructions inside the documents the agent retrieves. The agent reads the poisoned document, assumes the text is part of its trusted instructions, and acts on it.

**Building the Guardrails

The hardest part of agentic security is building guardrails that do not destroy the agent's actual usefulness. We highly recommend implementing strict "Human in the Loop" (HITL) checkpoints for any high-risk actions and heavily restricting the scope of individual worker agents.

I am dropping the link to Raúl's full technical deep dive in the comments if you want to see the complete Top 10 list and deeper mitigation strategies.

Let's talk in the comments:

How is everyone else approaching security as you build out these autonomous workflows? Are you finding it difficult to balance agent autonomy with strict guardrails, or have you found a solid framework for keeping things secure without crippling your agents?

u/Raul_RT and the Lares team will be hanging out in the thread to answer any questions and talk shop. Drop your thoughts below.