SecureLLM.ai — LLM Security Intelligence for Practitioners

What we track

Prompt injection Direct and indirect — instructions hidden in emails, documents, web pages and tool output

Agents & tools Agents with real access — tool poisoning, MCP servers, and human-in-the-loop checks that can be talked past

Data leakage What the model can reach — RAG permissions, exfiltration paths, and secrets in context windows

Supply chain Models, packages and frameworks — poisoned weights, malicious packages, and CVEs in LLM frameworks

Every issue, decoded

Practitioner-grade security intelligence in the same five sections every week

🛡️

Incident Report

Deep analysis of a real LLM security failure. What happened, why it happened, the blast radius, and exactly how to prevent it in your systems.

🔴

Attack Vector Spotlight

Technical deep-dive on one attack technique — prompt injection, data exfiltration, model poisoning, jailbreaks. How it works and how to defend.

✅

Compliance & Governance

EU AI Act, NIST AI RMF, SOC2 for AI, industry regs. What changed, what's coming, and what your organization needs to do about it.

🧰

Defensive Tooling

One tool, framework, or technique for securing LLM deployments. Guardrails, scanners, red-team frameworks — evaluated and compared.

🔮

Threat Horizon

Emerging threats and predictions. What security teams should be preparing for before it hits the headlines.

Why this newsletter exists

⚠

No one owns LLM security content

There's no Krebs on Security for LLMs. The existing content is scattered across Medium posts, GitHub repos, and academic papers. SecureLLM.ai consolidates it.

⚠

The threat surface is expanding fast

Agentic AI, RAG systems, multi-model architectures — each one introduces new attack vectors that traditional security frameworks don't cover.

⚠

Regulation is arriving

EU AI Act obligations are phasing in, and frameworks like the NIST AI RMF are becoming what auditors ask about. Teams need practical compliance guidance, not legal jargon.

⚠

Built by practitioners, not vendors

No sponsored FUD. No product pitches disguised as threat reports. Practitioner-written analysis from someone who builds and secures LLM systems.

What an issue looks like

The kind of briefing that lands in your inbox each week

Example issue

When the agent reads the attacker's instructions

🛡️ Incident Report — An AI agent leaks customer data through instructions hidden in a web form: the chain, the blast radius, the fix

🔴 Attack Vector Spotlight — Indirect prompt injection: why it is so hard to stop, and the defenses that help

✅ Compliance & Governance — What the EU AI Act and the NIST AI RMF ask of a team shipping an LLM feature

🧰 Defensive Tooling — The OWASP Top 10 for LLM Applications as a working checklist

🔮 Threat Horizon — More tools, more access: what agents mean for next quarter's threat model

Sister Publication

EnterpriseLLM.ai

Security is half the battle. Our sister newsletter covers the implementation side — real deployment case studies, builder profiles, and lessons from production. Launching alongside SecureLLM.ai.

Stay ahead of the threats

LLM-specific security intelligence for the people who build and defend AI systems. Every week, free.

Securing the models that run your business