Attackers can pre-register resource names that LLMs predictably hallucinate, turning agentic AI assistants into unwitting consumers of malicious promptware payloads.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
The CIA security triad - Confidentiality, Integrity, and Availability - is a cornerstone of data and cybersecurity. With the emergence of large language model (LLM) applications, a new class of threat, known as prompt injection, was first identified in 2022. Since then, numerous real-world vulnerabilities and exploits have been documented in production LLM systems, including those from leading vendors like OpenAI, Microsoft, Anthropic and Google. This paper compiles real-world exploits and proof-of concept examples, based on the research conducted and publicly documented by the author, demonstrating how prompt injection undermines the CIA triad and poses ongoing risks to cybersecurity and AI systems at large.
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cs.CR 2years
2026 2representative citing papers
A pipeline trains general-purpose red teaming models by finetuning small LLMs like Qwen3-8B to generate attacks for both seen and unseen adversarial objectives without relying on existing evaluators.
citing papers explorer
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Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting
Attackers can pre-register resource names that LLMs predictably hallucinate, turning agentic AI assistants into unwitting consumers of malicious promptware payloads.
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Training a General Purpose Automated Red Teaming Model
A pipeline trains general-purpose red teaming models by finetuning small LLMs like Qwen3-8B to generate attacks for both seen and unseen adversarial objectives without relying on existing evaluators.