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cs.CR2026
ASPIRE: Agentic Safety & Prompt Injection Red-teaming Engine
Pengfei He, Deep Mitra, Vishesh Sharma +4
LLM agents retrieve untrusted content and act through tools, creating indirect prompt-injection risks that can cause unauthorized actions or persistent state changes. Existing auto…
cs.CR2026
Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI
Mohan Rajagopalan, Vinay Rao
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel…
cs.CR2026
Authenticated Workflows: A Systems Approach to Protecting Agentic AI
Mohan Rajagopalan, Vinay Rao
Agentic AI systems automate enterprise workflows but existing defenses--guardrails, semantic filters--are probabilistic and routinely bypassed. We introduce authenticated workflows…