📌 LLM Vulnerabilities: how AI apps break — and how to harden them

This piece maps the most common ways LLM-powered systems fail in the real world and turns them into a practical hardening plan. From prompt and indirect injection to over-privileged tools, leaky RAG pipelines, data poisoning, jailbreaks, and supply-chain traps — plus the guardrails that actually help in production.

Key takeaways:
1️⃣ Prompt & indirect injection: attackers hide instructions in web pages, files, or retrieved notes; the model obeys them and exfiltrates secrets or performs unwanted actions.
2️⃣ Jailbreaks & policy evasion: harmless-looking reformulations bypass safety layers; outputs become unsafe or operationally risky.
3️⃣ RAG data leaks: sloppy retrieval exposes internal docs, customer data, and system prompts; cross-tenant bleed is a real risk.
4️⃣ Over-privileged tools/agents: broad filesystem, network, or payment permissions turn one prompt into a breach.
5️⃣ Poisoning & supply chain: tainted datasets, third-party prompts, and unpinned models/extensions undermine trust.
6️⃣ Output trust & hallucinations: fabricated facts sneak into workflows, tickets, or code — and humans often rubber-stamp them.
7️⃣ Telemetry gaps: without red-team sims and runtime monitoring, you won’t see injection attempts until damage is done.

👉 Read here: https://netlas.io/blog/llm_vulnerabilities/