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January 11, 2026

As AI becomes core infrastructure in 2026, attackers have learned to weaponize model compute costs. LLM10: Unbounded Consumption highlights how adversaries can trigger “Denial‑of‑Wallet” attacks by forcing models into expensive reasoning loops, overloading context windows, or causing agents to spam tool calls. This post outlines essential safeguards including hard token caps, token‑based rate limiting, execution timeouts, recursion limits, and semantic caching. The emerging best practice—model cascading—routes each request to the cheapest model capable of handling it, dramatically reducing risk and spend. The goal: keep your AI powerful, affordable, and resilient.

January 10, 2026

AI hallucinations have evolved from harmless quirks into serious enterprise risks. LLMs can generate confident but false information, fabricate sources, adopt user‑provided misinformation, and present themselves as authoritative experts—creating legal, operational, and brand liabilities. This post outlines modern defenses such as grounding models with advanced RAG, using Generator‑Critic architectures, applying faithfulness metrics, and enforcing deterministic guardrails. The emerging 2026 standard—the “Truth Anchor”—ensures every AI answer is tied to a verified source. The message is clear: trust your AI’s output only when it’s independently validated.

January 9, 2026

Vector database has become the most sensitive part of the GenAI stack—because embeddings effectively represent your company’s private documents, and attackers can now reverse‑engineer them with high accuracy. This post explains the key risks: vector poisoning, insecure retrieval, and semantic manipulation that lets attackers influence or extract the AI’s “memory.” It outlines modern defenses such as permission‑aware RAG with metadata filtering, row‑level security, anomaly detection on retrieval patterns, and encryption of embeddings at rest and in transit. The core message: protect your vector store like your most valuable database—because it is your AI’s source of truth.

January 8, 2026

System prompts have become the hidden rulebooks that govern AI behavior—and leaking them exposes your security logic, business rules, and guardrails to attackers. This post explains how adversaries extract prompt content through meta‑prompts, translation tricks, and side‑channel questioning. It outlines modern defenses including prompt masking, output scanning, refusal/deflection training, and moving critical logic out of prompts and into backend code. The key message: treat system prompts as inherently insecure and architect your AI so that a leaked prompt cannot compromise your application.

January 7, 2026

As autonomous AI agents grow more powerful, the biggest danger comes from giving them too much capability, too many permissions, or too much independence. This post explains how “Excessive Agency” emerges when agents receive broad tool access, privileged service accounts, or unchecked autonomy—creating serious security risks when combined with prompt injection or hallucinations. The solution is enforcing the Principle of Least Privilege: granular task‑specific tools, identity‑bound agents with scoped credentials, mandatory human‑in‑the‑loop approvals, and sandboxed execution environments. Proper governance ensures AI agents act safely, predictably, and only within the boundaries intended.

January 6, 2026

One of the biggest AI risks isn’t what users type into an LLM—it’s what the LLM outputs. Improper Output Handling allows malicious model responses to become executable code, enabling XSS, SSRF, RCE, or even destructive SQL queries. This blog explains how attackers use prompt injection to turn LLMs into payload generators and outlines essential defenses such as treating AI output as untrusted input, enforcing strict schemas, applying HTML/JS encoding, and using strong Content Security Policies. The core message: your application—not the AI—must be the final security gatekeeper.