Second-Order Injection: Attacking the Evaluator in LLM Safety Monitors

<h1> Static Analysis for LLM Prompt Security: A Methodology for Pre-Deploy Vulnerability Detection </h1> <p><em>How applying SAST principles to LLM prompt strings catches security

<p>Every prompt injection defense I’ve seen makes the same mistake. It asks the wrong question.<br /> The wrong question: “Does this prompt contain dangerous words?”<br /> The

<!-- SC_OFF --><div class="md"><p>When dealing with untrusted outside input, I think you should handle it based on the situation. If you're processing structured data files, it's

<p><em>Originally published on <a href="https://tamiz.pro/insights/prompt-injection-resilient-ai-applications" rel="noopener noreferrer">tamiz.pro</a>.</em></p> <p>The rapid

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining

<!-- SC_OFF --><div class="md"><p>I set out to test whether AAVE-coded (African American English Vernacular) prompts cause MoE language models to route, deliberate, and respond

arXiv:2610.04083v1 Announce Type: new Abstract: Memory poisoning attacks on LLM agents typically assume an external adversary who plants content in the agent's persistent memory