Unbounded Losses, Unchecked Inputs: Measuring the Safeguards of LLM Options-Trading Agents
Large language model (LLM) agents now place options orders at retail brokers, and developers publish open-source options agents faster than anyone has examined them.
ProofPaper ↗
Key points
- Prior security work on trading agents scored academic equities designs by whether an attacker can flip a buy-or-sell decision.
- We present the first measurement of the options agents that brokers and developers ship, scored by the safeguards they enforce and by the worst loss their documented order paths can reach.
- We screened 765 GitHub repositories, broker documentation, and the research literature and found 215 eligible repositories and 8 broker-native agent interfaces.
- Because an attacker who writes the agent's inputs can move an order's strike, expiry, volatility, and legs while its direction stays correct, options agents need loss limits enforced at the order boundary.
Sources (1)
- [1]Unbounded Losses, Unchecked Inputs: Measuring the Safeguards of LLM Options-Trading AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 10:38 PM
Large language model (LLM) agents now place options orders at retail brokers, and developers publish open-source options agents faster than anyone has examined them.
Prior security work on trading agents scored academic equities designs by whether an attacker can flip a buy-or-sell decision.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 16, 2026SPIDES: Information-Flow Control for Browser Agents
- Oct 16, 2026AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment
- Oct 16, 2026Model Capacity Frontier: A Development Framework for LLM-Supported Social Simulation
- Oct 16, 2026SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
- Oct 16, 2026OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
- Oct 15, 2026Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint