arXiv:2609.19705v2 Announce Type: replace Abstract: Large language model (LLM) agents already trade with real capital, yet agent-security research has mostly treated them as generic agents. Trading changes the threat: a compromised agent's orders move prices that other participants act on, and an attacker who profits from a crash can trigger the shock that a non-robust agent mishandles. We present FARSIGHT (Financial Agent Robustness and Security Investigation and Global Holistic Taxonomy), a systematization that rates trading agents on robustness to market stress and on security against source manipulation, agent compromise, and agents acting as attackers. It also checks paper-level ratings against released code. Across 15 academic schemes, none earns full credit on any robustness metric, no paper describes a way to halt trading without waiting for LLM inference, and none combines source provenance with cross-source corroboration. In the code of the 7 open-source schemes, no release enforces a stop-loss or position limit outside the LLM, 4 lack a component that their paper or documentation describes, and 2 run model-written code on the host. The same gaps appear beyond finance. Restating FARSIGHT as a domain-independent threat model, we map them onto agents in healthcare, law, software, and crypto. We then trace how compromised agents spread harm, as worms and as cascades that need no further compromise. At published injection rates per hop, a channel read by ten agents lets one compromised agent start an outbreak. We derive 14 open problems and urge deployers to corroborate sources, keep interrupts that bypass the model, and enforce loss limits outside it.