Tips to Secure Agentic AI Systems

Whether you’re integrating a third-party AI model or deploying your own, adopt these practices to shrink your exposed surfaces to attackers and hackers: • Least-Privilege Agents – Restrict what your chatbot or autonomous agent can see and do. Sensitive actions should require a human click-through. • Clean Data In, Clean Model Out – Source training data from vetted repositories, hash-lock snapshots, and run red-team evaluations before every release. • Treat AI Code Like Stranger Code – Scan, review, and pin dependency hashes for anything an LLM suggests. New packages go in a sandbox first. • Throttle & Watermark – Rate-limit API calls, embed canary strings, and monitor for extraction patterns so rivals can’t clone your model overnight. • Choose Privacy-First Vendors – Look for differential privacy, “machine unlearning,” and clear audit trails—then mask sensitive data before you ever hit Send. Rapid-fire user checklist: verify vendor audits, separate test vs. prod, log every prompt/response, keep SDKs patched, and train your team to spot suspicious prompts. AI security is a shared-responsibility model, just like the cloud. Harden your pipeline, gate your permissions, and give every line of AI-generated output the same scrutiny you’d give a pull request. Your future self (and your CISO) will thank you. 🚀🔐

Yet another day, yet another AI agent exploited: The EchoLeak vulnerability in Microsoft Copilot is just the latest proof that these attacks are no longer rare—they’re becoming the norm. Attackers didn’t need malware or phishing; a single crafted email was enough to manipulate Copilot into leaking sensitive data, all within its authorized scope. This is the new reality: * AI agents are being weaponized routinely, often with zero user interaction. * Traditional controls like OAuth and scopes can’t stop agents from being tricked into misusing their access. * Zero Trust isn’t optional: It requires all three—strict authentication, least-privilege authorization, and continuous monitoring—to catch hijacked agents in real-time. How to adapt: * Continuously audit MCP integrations for new abuse paths. * Enforce real-time guardrails to catch abnormal agent behavior. * Harden prompts and strictly isolate sensitive workflows. EchoLeak isn’t an outlier—it’s a sign of what’s next. Are you treating agentic AI as the security risk it now is? #AgenticAI #APISecurity #BotManagement #MCP #AISecurity #ZeroTrust

🚨 Your AI agents are sitting ducks for attackers. Here's what nobody is talking about: while everyone's rushing to deploy AI agents in production, almost no one is securing them properly. The attack vectors are terrifying. Think about it. Your AI agent can now: Write and execute code on your servers Access your databases and APIs Process emails from unknown senders Make autonomous business decisions Handle sensitive customer data Traditional security? Useless here. Chat moderation tools were built for conversations, not for autonomous systems that can literally rewrite your infrastructure. Meta saw this coming. They built LlamaFirewall specifically for production AI agents. Not as a side project, but as the security backbone for their own agent deployments. This isn't your typical "block bad words" approach. LlamaFirewall operates at the system level with three core guardrails: PromptGuard 2 catches sophisticated injection attacks that would slip past conventional filters. State-of-the-art detection that actually works in production. Agent Alignment Checks audit the agent's reasoning process in real-time. This is revolutionary - it can detect when an agent's goals have been hijacked by malicious inputs before any damage is done. CodeShield scans every line of AI-generated code for vulnerabilities across 8 programming languages. Static analysis that happens as fast as the code is generated. Plus custom scanners you can configure for your specific threat model. The architecture is modular, so you're not locked into a one-size-fits-all solution. You can compose exactly the protection you need without sacrificing performance. The reality is stark: AI agents represent a new attack surface that most security teams aren't prepared for. Traditional perimeter security assumes humans are making the decisions. But when autonomous agents can generate code, access APIs, and process untrusted data, the threat model fundamentally changes. Organizations need to start thinking about AI agent security as a distinct discipline - not just an extension of existing security practices. This means implementing guardrails at multiple layers: input validation, reasoning auditing, output scanning, and action controls. For those looking to understand implementation details, there are technical resources emerging that cover practical approaches to AI agent security, including hands-on examples with frameworks like LlamaFirewall. The shift toward autonomous AI systems is happening whether security teams are ready or not. What's your take on AI agent security? Are you seeing these risks in your organization? For the full tutorial on Llama Firewall: Tutorial: https://lnkd.in/evUrVUb9 Huge thanks to Matan Kotick Amit Ziv for creating it! ♻️ Share to let others know it!

Just finished reading the Agentic AI Red Teaming Guide from Cloud Security Alliance and it’s one of the most practical breakdowns I’ve seen on securing autonomous AI systems. Most of the AI conversation focuses on what agents can do. This digs into what can go sideways — and how to actually test for it before it hits production. The report outlines 12 categories of risks things like: → Control hijacking (agents taking unauthorized actions) → Goal manipulation (small prompt tweaks shifting entire behaviors) → Hallucination chains (one made-up output triggering a whole series of bad decisions) → Memory and context attacks (cross-session data leaks, poisoned recall) → Weak fallback and alert systems (especially when checkers get left out of the loop) What I liked most? It’s not just theory. There are example prompts, attack flows, and red-teaming steps teams can actually try out. Some takeaways that stuck with me: → Don’t let agents carry over roles or memory they shouldn’t → If your agents are writing to prod systems — you better be tracing, logging, and isolating → Validate all your external data sources, even if they’re “trusted” → Red team the agent, not just the LLM If you’re building or evaluating agentic systems, give this a read. It’ll make you rethink how these agents behave and how you secure them. Kudos to Ken Huang, CISSP, OWASP, CSA, and the whole crew behind this. Solid work.

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.