OTora provides the first unified framework for reasoning-level denial-of-service attacks on LLM agents, achieving up to 10x more reasoning tokens and order-of-magnitude latency increases while preserving task accuracy across multiple agent types and models.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.
Pruning removes 'unsafe tickets' from LLMs via gradient-free attribution, reducing harmful outputs and jailbreak vulnerability with minimal utility loss.
citing papers explorer
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OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents
OTora provides the first unified framework for reasoning-level denial-of-service attacks on LLM agents, achieving up to 10x more reasoning tokens and order-of-magnitude latency increases while preserving task accuracy across multiple agent types and models.
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GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.
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Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs
Pruning removes 'unsafe tickets' from LLMs via gradient-free attribution, reducing harmful outputs and jailbreak vulnerability with minimal utility loss.