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EVMbench: Evaluating AI Agents on Smart Contract Security

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

citation-role summary

dataset 2 background 1

citation-polarity summary

fields

cs.CR 2 cs.AI 1

years

2026 3

representative citing papers

Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain

cs.CR · 2026-05-09 · accept · novelty 7.0

The paper delivers a systematization of knowledge on AI agent-blockchain interactions via a bidirectional trust framework, an Agent-Blockchain Interaction Model, a five-dimensional evaluation lens, and nine identified open problems.

Alignment Contracts for Agentic Security Systems

cs.CR · 2026-04-30 · conditional · novelty 6.0

Alignment contracts define scope, allowed effects, budgets and disclosure rules as safety properties over finite effect traces, with decidable admissibility, refinement rules, and Lean-verified soundness under an observability assumption.

citing papers explorer

Showing 3 of 3 citing papers.

  • Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain cs.CR · 2026-05-09 · accept · none · ref 143

    The paper delivers a systematization of knowledge on AI agent-blockchain interactions via a bidirectional trust framework, an Agent-Blockchain Interaction Model, a five-dimensional evaluation lens, and nine identified open problems.

  • Alignment Contracts for Agentic Security Systems cs.CR · 2026-04-30 · conditional · full · ref 44

    Alignment contracts define scope, allowed effects, budgets and disclosure rules as safety properties over finite effect traces, with decidable admissibility, refinement rules, and Lean-verified soundness under an observability assumption.

  • CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing cs.AI · 2026-05-10 · unverdicted · none · ref 13

    Chaintrix achieves 71.7% recall on 120 high-severity vulnerabilities in the EVMbench benchmark and outperforms the strongest frontier-model baseline by 26 percentage points through LLM pipelines grounded in a Cross-Contract Interaction Model and filtered by structural checks.