Real research from Unit 42 and the security community — explained plainly, so your team knows what's coming.
Board-level and operator-level explainers on prompt injection, jailbreak bypass, unsafe tool use, shadow AI, and compliance evidence.
Security research translated for product, platform, and risk teams without losing the important details.
Ten live articles covering the highest-risk failure modes teams face while moving LLM systems into production.
Articles 1 through 10 are live now. The hub is fully populated while the rest of the Unit 42 content pool stays queued for the next publishing slots.
Prompt injection attacks let user-controlled text rewrite what your AI system treats as the highest priority. For SaaS teams shipping copilots, support bots, or agent workflows, that makes prompt injection one of the clearest LLM security risks on the board agenda.
The attacker does not need to type instructions into the chat box. In agentic systems, the dangerous prompt can be hidden inside a support ticket, uploaded document, or retrieved page your assistant decides to trust.
Once an LLM can read content and act through tools, prompt injection stops being a bad answer problem and becomes an attacker operating with your company's real permissions.
Safety filters feel like a moat, but in production they are often just a speedbump. This article explains how multi-turn and roleplay jailbreaks bypass static controls and why security leaders need evidence beyond moderation settings.
The OWASP LLM Top 10 is quickly becoming the common language for AI risk review. If your team cannot map LLM controls, adversarial testing, and evidence to it before a SOC 2 Type II audit, you are walking in unprepared.
Traditional scanners were built for CVEs, ports, headers, and exposed services. LLM risk lives in behavior: prompt injection, jailbreaks, indirect context attacks, and tool misuse that only show up when the system is tested adversarially.
For SaaS teams selling into Europe, 2026 is when AI compliance stops being a slide-deck topic and becomes an evidence problem. The hard part is not knowing the AI Act exists. It is proving what category your system falls into, what risks you tested, and what records you can produce when a buyer, auditor, or regulator asks.
The most immediate AI risk for many companies is not the model they approved. It is the one employees already use through browser tabs, plugins, meeting assistants, and coding tools that sit outside normal security, privacy, and procurement review.
Most first-time AI launches do not fail because the team ignored security entirely. They fail because nobody forced the design review to answer ten predictable questions about prompt injection surface area, tool permissions, retrieval integrity, output sanitization, and incident response before go-live.
Unit 42 highlights a compounding failure mode in multi-agent AI systems: compromise one model in the chain, and downstream agents can inherit attacker-written intent before a human ever sees the output.
Most teams discover critical prompt injection vulnerabilities in their first Ciphvex audit.