Hey dev.to community! As we hit February 2026, agentic AI isn't just hype – it's in production, autonomously handling tasks from data queries to code execution. But with great power comes... massive vulnerabilities.
In my new post on CloudWorld13, I break down the 7 critical attack surfaces most teams ignore:
Prompt Injection Evolution: Semantic poisoning in trusted data streams (think hidden code in emails/databases).
Tool Misuse & API Abuse: When your agent's SQL access turns into an exfiltration tool.
Machine Identity Hijacking: Non-human creds outnumber humans 10:1 – rotate or regret.
Multi-Agent Risks: Swarm poison leading to cascading failures.
Memory Poisoning: Corrupting long-term context for persistent attacks.
Supply Chain Integrity: Verifying model weights before deployment (hash 'em!).
Output Hallucination Exploits: Sandbox actions to block unauthorized moves.
Plus, a step-by-step playbook: Map identities, enforce guardrails, isolate swarms, and red-team continuously. Includes tables on impacts, mitigations, and stats (e.g., 35% incidents from identity abuse).
If you're building agentic systems with LangChain, AutoGen, or custom stacks, this is your wake-up call. What's your go-to defense against prompt hijacking? Drop code snippets or thoughts below!
Read the full article: agentic AI security
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