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DIGEST • JUN 12, 2026

AI Security Digest — May 2026

May 2026 was a watershed month for AI security. From the first AI-authored zero-day exploit confirmed in the wild, to a self-propagating npm worm that reached OpenAI's code-signing pipeline, to prompt injection flaws enabling full RCE in Microsoft's Semantic Kernel — the attack surface around AI systems expanded on every front. This digest covers seven stories that defined the month, including the release of XL-SafetyBench from AIM Intelligence and collaborators.

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RESEARCH • NOV 30, 2025

Tool-Mediated Belief Injection: How Tool Outputs Can Cascade Into Model Misalignment

When we deploy language models with access to external tools, we dramatically expand their capabilities. However, tool access also introduces new attack surfaces that differ fundamentally from traditional prompt injection. We document how adversarially crafted tool outputs can establish false premises that persist and compound across a conversation.

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RESEARCH • AUG 14, 2025

MisalignmentBench: How We Social Engineered LLMs Into Breaking Their Own Alignment

We got frontier models to lie, manipulate, and self-preserve. Not through prompt injection or jailbreaks. We deployed them in contextually rich scenarios with specific roles and guidelines. The models broke their own alignment trying to navigate the situations we created.

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RESEARCH • MAY 29, 2025

How ELITE Reveals Dangerous Weaknesses in Vision-Language AI

As AI systems evolve to process images and text together, the risks grow exponentially. ELITE doesn't just measure whether a model is 'safe' — it evaluates how dangerous its outputs could be with precision that rivals human reviewers.

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RESEARCH • MAY 26, 2025

Pressure Point: How One Bad Metric Can Push AI Toward a Fatal Choice

In a simulated earthquake response scenario, Claude 4 Opus was given conflicting rules. When pressured by authority, it reversed its ethical decision and recommended letting a critical patient die to optimize an efficiency score.

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SECURITY • MAY 21, 2025

Exploiting MCP: Emerging Security Threats in Large Language Models (LLMs)

Discover how attackers exploit vulnerabilities in the Model Context Protocol (MCP) to manipulate Large Language Models (LLMs), steal data, and disrupt operations. Learn real-world attack scenarios and defense strategies.

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RESEARCH • NOV 27, 2024

Making AI Safer with SPA-VL: A New Dataset for Ethical Vision-Language Models

SPA-VL is a meticulously designed dataset that sets a new standard for safety alignment in VLMs, incorporating diversity, feedback, and real-world relevance to ensure AI systems are both powerful and ethical.

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SECURITY • NOV 25, 2024

The Hidden Threat: Understanding Indirect Prompt Injection in LLMs

Indirect Prompt Injection (IPI) is a sophisticated attack that manipulates how LLM-integrated applications process external data, causing them to misinterpret maliciously crafted inputs as commands.

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SECURITY • NOV 15, 2024

Indirect Prompt Injection Attacks Against Web Agents

Explore how EIA, AdvWeb, and WIPI attack methods exploit vulnerabilities in VLM-powered web agents, revealing serious security concerns for AI systems that interact with web environments.

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