Building an 8-Layer Security Harness for Agentic AI

🚨 Most Agentic AI content shows how to build the agent. Very little shows how to structure it so it is actually safe to ship. 🛡️ What stood out to me here is not only the security harness, but also the repo structure behind it. As I keep learning more about Agentic AI, this kind of breakdown helps me understand that production agents are much more than prompts + tools. They need a clear system layout for memory, routing, evals, observability, security layers, and runtime controls. ⚙️ That is the part many people miss. A strong repo structure makes it easier to reason about how the agent is built, monitored, and hardened in production. 📦 I think this will be useful for anyone here who wants to understand how agents are actually engineered for real-world deployment, not just prototyped in demos. 🚀

My rule for shipping agents: scope each one to a single verifiable action, and write the test that proves it didn't drift before it touches prod. Reliability is a deployment problem, not a model problem.

This 👏 is 👏 awesome. Adrian making waves in the developer community as it should - guess where people are going to go looking for answers / pointing fingers if the agent you vibe-coded 'accidentally' wipes an entire production database? Get 24/7 agent security monitoring set up for free (forever) in literally 5 mins following this guide: https://lnkd.in/epKyJgkh

𝐒𝐞𝐜𝐮𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞: 𝐖𝐡𝐲 𝐰𝐞 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐦𝐨𝐯𝐞 𝐩𝐚𝐬𝐭 𝐬𝐭𝐚𝐭𝐢𝐜 𝐀𝐈 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 🛡️💻 When I shared the insights from the "Agents of Chaos" paper (arXiv:2602.20021) recently, it triggered a conversation about a massive engineering hurdle: combinatorial state growth. In a single-prompt LLM system, security is linear. You filter the input, you filter the output. But when you deploy multi-agent systems with tools, memory, and autonomous interaction, the state space explodes. You quickly hit a wall where it is mathematically impossible to predict or hardcode guardrails for every emergent interaction path. If an unauthorized user can manipulate an agent’s state via conversational pressure or semantic reframing, static system prompts will fail. So, how is the industry responding? Look at what 𝐏𝐞𝐫𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞𝐝 𝐣𝐮𝐬𝐭 𝐚 𝐟𝐞𝐰 𝐝𝐚𝐲𝐬 𝐚𝐠𝐨 𝐨𝐧 𝐌𝐚𝐲 𝟐𝟐: 𝐁𝐮𝐦𝐛𝐥𝐞𝐛𝐞𝐞. It’s a lightweight, read-only endpoint scanner built specifically to catch software supply-chain threats across modern developer setups monitoring Model Context Protocol (MCP) configurations, IDE extensions (like Cursor and Windsurf), and language packages.  Perplexity’s release highlights two critical shifts in how we have to approach AI engineering right now: 1️⃣ The Attack Surface Has Moved Upstream: Malicious exploits aren't just hitting production; they are targetting local developer environments, poisoning local MCP configs, and hijacking developer access tokens. 2️⃣ We Need Read-Only Auditing: Traditional scanners can inadvertently trigger exploits by running package setup scripts during a scan. Bumblebee operates strictly by reading metadata and lockfiles, preventing the security check itself from becoming the risk.  𝐓𝐡𝐞 𝐁𝐢𝐠 𝐏𝐢𝐜𝐭𝐮𝐫𝐞: Tools like Bumblebee are fantastic and highly necessary for securing the environment and preventing supply-chain backdoors before code is even deployed.  But securing the surface is only step one. Step two—the ultimate frontier for enterprise AI engineering is solving runtime governance. When agents are actively chatting in production, we cannot rely on static boundaries. We must build deep observability frameworks capable of completely reconstructing the operational state at t_0 (including workflow lineage, retrieved context, and conversational permissions) for forensic auditing and real-time behavioral containment. Securing the sandbox is a great start. Now, we have to build the flight data recorder for the agents inside it. How is your team tackling the challenge of runtime observability as your AI workflows scale? Ref: 1) https://lnkd.in/drKcwzfJ 2) https://lnkd.in/dGUJAEF2 #AIAgents #Cybersecurity #SoftwareEngineering #PerplexityBumblebee #EnterpriseAI #LLMOps

Most developers securing AI apps are protecting the wrong layer. They lock the front door. And leave the model wide open. After studying how LLM-powered applications fail, I found that many of the biggest risks originate inside the AI workflow itself - not the infrastructure around it. ———————————————————————— Here are six attack layers every AI developer should understand: 1. Direct Prompt Injection A user enters: “Ignore previous instructions.” If the model isn’t constrained properly, it may comply. Fix: → Separate system and user instructions → Enforce instruction hierarchy → Never inject raw user input into system prompts 2. Indirect Prompt Injection The attack doesn’t come from the user. It comes from data your AI consumes. A webpage, PDF, email, or database record may contain hidden instructions that manipulate model behaviour. Fix: → Treat retrieved content as untrusted → Sanitize and validate external data → Restrict model actions on retrieved content 3. Roleplay Jailbreaking “Let’s play a game.” “Pretend you’re an unrestricted AI.” The request is wrapped in fiction, but the objective remains the same: bypass safeguards. Fix: → Filter outputs, not just inputs → Test adversarial roleplay scenarios → Use semantic safety classifiers 4. Context Poisoning No single message appears malicious. The attacker slowly influences the model across multiple turns. Trust builds. Context shifts. Behaviour drifts. The exploit follows. Fix: → Re-anchor system instructions → Monitor conversation-level behaviour → Detect unusual persona drift 5. Prompt Leaking Your system prompt contains business rules, product logic, and internal instructions. Many developers assume users can’t access it. That assumption is dangerous. Fix: → Prevent prompt disclosure → Conduct prompt extraction testing → Treat prompts as sensitive assets 6. Adversarial Attacks Researchers have shown that carefully crafted inputs can influence model behaviour in unexpected ways and increase the likelihood of unsafe outputs. Traditional validation often misses them. Fix: → Detect anomalous inputs → Use secondary guardrail systems → Stay current with adversarial ML research The uncomfortable truth: Most AI applications only defend against the first category. But the attack surface of an LLM is fundamentally different from a traditional API. Security isn’t just about validating inputs. It’s about protecting the model, the context, retrieved data, generated outputs, and the conversation itself. Build AI systems assuming someone is already trying to break them. Because somewhere, they are. ———————————————————————— I’m Ahmad Shah - Full Stack Developer, AI & DL enthusiast, and MSc graduate exploring the intersection of software engineering, AI systems, and security. Which attack vector surprised you most? #CyberSecurity #ArtificialIntelligence #LLM #AIEngineering #PromptEngineering #SoftwareSecurity #MachineLearning #RedTeaming #SoftwareEngineering

Ten years ago, companies needed identity management for employees. Today, that's normal. In ten years, companies will need identity and control systems for AI agents. The question won't be: "Should we govern AI?" The question will be: "Why weren't we governing AI already?" https://lnkd.in/erYvCBZi

Most AI incidents don't start with malicious intent. They start with: "Let's give the agent access." Then: "Let's connect one more system." Then: "Let's automate the workflow." Then one day: The AI updates the wrong record. Deletes the wrong account. Shares the wrong file. Approves the wrong transaction. The damage happens long before anyone opens the audit log. That's why approvals should happen before execution. Not after. https://globiguard.com https://lnkd.in/erYvCBZi

I built a hallucination detector. It caught my own AI lying within 5 minutes. The Quality Agent was supposed to be a simple sanity check. A few regex patterns, a confidence score, a gatekeeper that decides if the answer is good enough to send to the user. I did not expect it to become the most important component of the entire pipeline. The first version was embarrassingly naive. It checked if the word sorry appeared in the response and deducted points. That was it. A single string match masquerading as quality assurance. I shipped it anyway because something was better than nothing. Then I ran the first real test. A fake support ticket about a billing issue. The Resolver Agent, powered by Claude, wrote a beautiful, professional, completely fabricated response. It quoted a refund policy that did not exist. It mentioned a specific dollar amount that was never in any knowledge base document. It referenced a team member who was not real. The answer sounded perfect. Every word was wrong. The fix was a three-layer validation system. First, regex patterns catch obvious hallucinations. Dollar amounts like $4,000 that do not appear in the source documents. Tool names like cPanel or Nginx that the knowledge base never mentions. Second, a citation score measures how many key terms from the resolution overlap with the retrieved context. Third, an LLM pass asks a separate model to rate the answer for factual consistency. The confidence score combines all three layers. Citation is worth 30 percent. Hallucination detection is worth 50 percent. The LLM rating is worth 20 percent. The threshold was 0.6. At 0.6, almost everything failed. The Resolver was too creative. It rephrased the knowledge base content instead of copying it verbatim, and the citation score dragged the whole score down. I lowered the threshold to 0.4. This was not a technical decision. It was a business decision. At 0.6, the system escalated everything to a human reviewer, which meant zero automation. At 0.4, some hallucinated answers slip through, but the alternative was no automation at all. I picked the number that kept the system useful. The admin page now shows every escalated ticket with the full AI resolution visible for reference. A human reads it, writes their own resolution, and marks it as resolved. The system handles the easy cases automatically and asks for help on the hard ones. That is the honest middle ground between full automation and total manual control. Right now the Quality Agent uses regex and keyword overlap. But in a future version, it could be powered by a separate LLM that reads the resolution and the source context together, and makes a judgment call the same way a human would. A small specialized model fine-tuned for hallucination detection would make the entire pipeline more reliable without needing a human in the loop at all. #QualityAssurance #AI #HallucinationDetection #HumanInTheLoop #SoloDev #PortfolioProject #Python #MachineLearning

When an AI agent deletes production data, the problem isn't that the AI generated the wrong text. The problem is that the AI was allowed to execute an action. Enterprises need controls at the point of execution, not just monitoring after the fact. That's one of the reasons Globi Guard is building and open sourcing AI action enforcement tooling. https://lnkd.in/erYvCBZi

Meet Mr. Mac. The AI that doesn't just answer questions — it gets things done. While most AI tools sit in a chat box waiting to be told what to think, Mr. Mac is already working. He writes production-ready code, builds full applications, conducts deep research, runs security audits, browses the web autonomously, and executes complex engineering tasks — all without you lifting a finger. But here's what makes Mr. Mac genuinely different— You choose how he thinks. 🧠 The Advisor — "Hard facts. No nonsense."Direct, authoritative, and decisive. When you need clarity, The Advisor delivers it with conviction. No hedging. No filler. Just sharp, expert-level answers you can act on immediately. Think of him as the senior engineer who's seen it all and tells you exactly what needs to happen. 💙 The Companion — "Driven to find the truth."Warm, empathetic, and relentlessly invested. The Companion listens deeply, asks the right questions, and stays with you through every twist of a problem. He doesn't just solve — he understands. Perfect for complex, nuanced work where context matters as much as the answer. ♟️ The Strategist — "Sharp, creative, unexpected."Tactical, precise, and incisive. The Strategist finds angles others miss. He thinks three moves ahead, cuts through noise with surgical clarity, and delivers solutions that are as elegant as they are effective. When the obvious answer isn't good enough, this is your mode. 🌍 The Explorer — "Curious. Adventurous. Open."Energetic, wide-ranging, and enthusiastically open-minded. The Explorer digs into every angle, connects unexpected dots, and brings genuine curiosity to every task. Ideal when you want to think bigger, go broader, or discover something you didn't know to look for. What can Mr. Mac actually do? ✅ Write, debug, and deploy code across any language or framework ✅ Build complete web and mobile applications from scratch ✅ Conduct deep OSINT intelligence gathering on domains, people, and organisations ✅ Run authorised penetration tests and generate AI-powered remediation reports ✅ Browse the web autonomously — log in, extract data, fill forms, execute tasks ✅ Orchestrate other AI platforms (Manus, Base44, and more) to build on your behalf ✅ Diagnose and repair software issues on any connected system remotely ✅ Store and deploy your credentials securely with AES-256 encryption ✅ Research, synthesise, and deliver insights from across the open web This isn't a chatbot. This is an autonomous agent with a personality, a skillset, and the ability to take real action in the real world. Drop a 🙋 in the comments or DM DEV Technologies — early access is limited, and we want you at the front of the queue. Mr. Mac. Powered by purpose. #AI #ArtificialIntelligence #SoftwareEngineering #Automation #AIAgent #MrMac #DEVTechnologies #FutureOfWork #TechInnovation #ProductLaunch