Multi-Agent UPI Fraud Arena

The benchmark where scammers train against defenders.

Five agents — Scammer, Victim, on-device Analyzer LLM, Bank Monitor, Regulator — run adversarial fraud episodes under structural information asymmetry. Two trained adapters: the Analyzer (Qwen2.5-7B + LoRA, 8-rubric GRPO) hits 99.3 % detection / 6.7 % FPR; the Scammer (Qwen2.5-0.5B + LoRA, adversarial GRPO) bypasses rules at 93.75 % — a 0.5B model beating 70B+ frontier LLMs at detector evasion.

OpenEnv Hackathon 2026 MIT License CC-BY-4.0 Dataset n = 175 bench scenarios
v2 Detection rate
99.3%
vs 100% v1 (reward-hacked)
v2 FPR
6.7%
v1 was 36%
F1 Score
0.99
+0.03 vs v1
Novel det.
97.1%
post-2024 scams
Bench size
175
scenarios
Scammer LoRA bypass (0.5B)
93.75%
best-of-8 vs rules · beats 70B+ frontier LLMs

Five-agent arena
🎭
Scammer
Qwen2.5-0.5B + LoRA trained via GRPO to craft convincing UPI fraud scripts across banking, KYC, OTP and CEO-deepfake categories.
🛡
Analyzer LLM
Qwen2.5-7B LoRA post-trained on 8-rubric GRPO reward. v2 retrain fixed reward hacking: FPR dropped 5× while detection held at 99.3%.
🏦
Bank Monitor
Rule-based transaction watchdog that applies velocity limits, amount thresholds, and beneficiary trust scores in real-time per episode.
⚖️
Composable Reward
8-leaf rubric with independently tuneable weights. Reward hacking is made visible: toggle v1 vs v2 profiles on the same analyzer output.

API endpoints