Research
GPT-5 Nano IPI Assessment — LLM Vulnerability Research
Publication date not yet evaluated · Added lateos.ai
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Single-source research — one report is available.
Why it matters
It quantifies where a deployed LLM's guard stack fails against indirect prompt injection and MCP tool poisoning, helping defenders prioritize mitigation for agentic deployments.
A black-box prompt injection susceptibility assessment of GPT-5 Nano using the IPI Taxonomy v0.13 across 201 analyzed test cases, reporting a 38.3% overall susceptibility rate. The model was fully resistant to surface-level attacks (CSS concealment, HTML cloaking, SEO phishing, RAG corpus poisoning) but highly vulnerable to recursive instruction framing (100%) and MCP tool description poisoning (80%).