arXiv:2507.05558v1 Announce Type: cross Abstract: We present A1, an agentic execution driven system that transforms any LLM into an end-to-end exploit generator. A1 has no hand-crafted heuristics and provides the agent with six domain-specific tools that enable autonomous vulnerability discovery. The agent can flexibly leverage these tools to understand smart contract behavior, generate exploit strategies, test them on blockchain states, and refine approaches based on execution feedback. All outputs are concretely validated to eliminate false positives. The evaluation across 36 real-world vulnerable contracts on Ethereum and Binance Smart Chain demonstrates a 62.96% (17 out of 27) success rate on the VERITE benchmark. Beyond the VERITE dataset, A1 identified 9 additional vulnerable contracts, with 5 cases occurring after the strongest model's training cutoff date. Across all 26 successful cases, A1 extracts up to 8.59 million USD per case and 9.33 million USD total. Through 432 experiments across six LLMs, we analyze iteration-wise performance showing diminishing returns with average marginal gains of +9.7%, +3.7%, +5.1%, and +2.8% for iterations 2-5 respectively, with per-experiment costs ranging $0.01-$3.59. A Monte Carlo analysis of 19 historical attacks shows success probabilities of 85.9%-88.8% without detection delays. We investigate whether an attacker or a defender benefits most from deploying A1 as a continuous on-chain scanning system. Our model shows that OpenAI's o3-pro maintains profitability up to a 30.0 days scanning delay at 0.100% vulnerability incidence rates, while faster models require >=1.000% rates to break-even. The findings exposes a troubling asymmetry: at 0.1% vulnerability rates, attackers achieve an on-chain scanning profitability at a $6000 exploit value, while defenders require $60000, raising fundamental questions about whether AI agents inevitably favor exploitation over defense.
