SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance
Existing LRM-DoS methods rely heavily on model feedback to synthesize attack queries, requiring either repeated queries to the target model or training a dedicated attack model. These expensive operations severely weaken attack leverage. In this paper, we propose \emph{search amplification}, a novel, model-feedback-free LRM-DoS paradigm. It employs the conflict count derived from an Satisfiability Modulo Theories (SMT) solver as a low-cost external signal to guide the synthesis of inference-heavy Constraint Satisfaction Problem (CSP) instances. Our key observation is that LRMs depend on trial-
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- PossiblePossibly related (embedding) · 48%IEEE Rolls Out Large Language Models Virtual Training Course →
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“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
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- LinkedLinked via arxiv author · 85%Peng-Jian Yang →
“SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance”
- LinkedLinked via arxiv author · 85%Zhenqi Feng →
“SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance”
- LinkedLinked via arxiv author · 85%Zhaoyang Yu →
“SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance”
- LinkedLinked via arxiv author · 85%Zhaoxin Fan →
“SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance”
- LinkedLinked via arxiv author · 85%Kejian Wu →
“SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance”