RAG security
Retrieval-augmented generation usually fails in the index and the tool layer, not in a clever chat prompt.
This section keeps the pipeline guide, the retrieval-time controls, and the case for why a prompt firewall does not cover poisoned documents or long-term memory.
AI Red Teams Are Standardizing on Structured Output Attacks
Attackers are no longer just trying to jailbreak a model’s text—they’re targeting the JSON, XML, and function-call formats that modern AI systems trust downstream. Security teams need to understand how structured outputs can silently turn a harmless-looking response into unsafe automation or data leakage.
AI Vulnerability Management Needs an Exposure Map, Not Another Scanner
The latest AI security warnings suggest the real problem isn’t finding one more model flaw—it’s tracking how model endpoints, plugins, vectors, and agent permissions compound into a breach path. Security teams that can map and prioritize that exposure may be the only ones ready when the next AI bug becomes an incident.
LLM Red Teaming Is Shifting Toward Multi-Turn Jailbreaks
Static prompt filters catch obvious attacks, but newer jailbreaks chain roleplay, context poisoning, and tool abuse across several turns to slip past them. Security teams now need red-team tests that measure how models behave over an entire conversation, not just one prompt.
RAG Security in 2026: How to Stop Prompt Injection at Retrieval Time
Prompt injection is no longer just a chatbot problem—it can poison retrieval pipelines, leak sensitive context, and steer downstream actions. This post examines practical defenses for securing RAG systems before attackers turn your vector store into an attack path.
Guardrailing RAG in 2026: Why Prompt Firewalls Aren’t Enough
Attackers are moving past simple prompt injection and exploiting retrieval, tool calls, and memory to steer LLM apps. This post shows why AI security teams now need retrieval-level controls, policy checks, and continuous red-teaming to keep RAG systems safe.
Securing RAG Pipelines: Poisoned Vectors, Prompt Injection, Exfiltration
A single malicious document in your vector store can steer answers, leak hidden instructions, or even exfiltrate sensitive data through a carefully crafted query. This post breaks down where RAG breaks first—and the concrete controls that stop poisoned retrieval, indirect prompt injection, and unauthorized data leakage.