Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How to Prevent Prompt Injection in AI Agents

In agentic architectures, model behavior is guided by a combination of system prompts, retrieved context, and tool-related inputs rather than a single instruction source. When signals conflict or include untrusted instructions, models must infer which inputs to follow. This ambiguity exposes an opening for prompt injection attacks.

IT Giveth, Security Taketh: The Hidden Cost of Configuration Drift

“IT giveth. Security taketh.” A topic examined in a print interview with Colt Blackmore, co-founder & CTO of Reach Security, written by Dan Raywood at Security Boulevard: ︎ The long-standing friction between IT enablement and security restriction︎ Configuration drift as the quiet divergence between intended and actual state︎ How incremental change accumulates into measurable risk︎ The challenge of maintaining alignment in complex, fast-moving environments︎ Why drift often remains invisible until consequences surface.

Moltbook Data Exposure - The 443 Podcast - Episode 357

This week on the podcast, we cover a recent supply chain compromise involving the popular text editor Notepad++. After that, we discuss a recent vulnerability report in the Moltbook AI social network before ending with a deep-dive review of a recent remote code execution vulnerability in the N8N automation platform.

AI Agents Are The New Detection Problem Nobody Designed For

AI agents now operate as core identities in enterprise environments, authenticating, accessing data, and executing workflows at machine speed. Their flexibility and scale introduce a detection challenge traditional security models were never built to solve. Exabeam has seen this pattern before with insider threat and workload identities. AI agents accelerate the need for identity-centric detection.

International AI Safety Report 2026: What It Means for Autonomous AI Systems

The International AI Safety Report 2026 is one of the most comprehensive overviews to date of the risks posed by general-purpose AI systems. It’s compiled by over 100 independent experts from more than 30 countries, and shows that while AI systems are performing at levels that seemed like science fiction only a few years ago, the risks of misuse, malfunction, and systematic and cross-border harms are clear. It makes a compelling case for better evaluation, transparency, and guardrails.

Intelligent AI Routing Rules That Pick the Cheapest Model That Still Meets Quality (with Practical Examples)

Most teams do one of two things with LLMs: they pick one "safe" premium model and accept the bill, or they swap models by hand and hope nothing breaks. Both approaches get old fast when traffic grows, prices change, or one provider has a rough day. Intelligent routing rules fix that by making model choice automatic. Instead of "always use Model X," you set constraints like price, latency budget, context window, and a minimum quality bar. Each request gets the cheapest model that can still do the job, and it escalates only when it needs to.

You can't rely on open source for security - not even when AI is involved

Open source libraries, packages, and models power nearly every product team today. They accelerate development, democratize innovation, and let teams stand on the shoulders of giants. But there’s a dangerous assumption creeping into engineering orgs: that open source — or AI trained on open source — will keep your software safe. That assumption is wrong. Open source gives you speed and community, not guaranteed security.

How autonomous AI agents like OpenClaw are reshaping enterprise identity security

The viral surge of OpenClaw (formerly Clawdbot and Moltbot) has captured the tech world’s imagination, amassing over 160,000 GitHub stars and driving a hardware rush for Mac Minis to host these 24/7 assistants.