DICE: Tilting the Odds through Interface Attackson System One Model
cs.CR, cs.SE
Submitted: 2026-10-09
Updated: 2026-10-09
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: System One models offer typed judgments that applications can turn into decisions.
Abstract
System One models offer typed judgments that applications can turn into decisions. Their answers, however, are conditional on the state and question supplied by surrounding components, and on how the application uses the response. We study this boundary in a Jev-based pipeline with bounded substitutions at three adapter outputs: state extraction, question generation, and output interpretation. Across three small synthetic studies, each adapter can change the final decision while Jev and its authentic API responses remain outside attacker control. The cases expose distinct failures of source fidelity, answer meaning, and response relevance. Checks that bind these relationships stopped the corresponding interventions we tested, although source verification also rejected some valid clean decisions. The pilot identifies where correct request-level judgments can lose their connection to the original task; it does not estimate the prevalence of these failures in deployed systems.
Sources
- Evaluating and Benchmarking the System One Model Jev
- Evaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation
- JudgeLM: Fine-tuned Large Language Models are Scalable Judges
- RouteLLM: Learning to Route LLMs with Preference Data
- Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
- ReAct: Synergizing Reasoning and Acting in Language Models
- Universal and Transferable Adversarial Attacks on Aligned Language Models
- The Structured Output Benchmark: A Multi-Source Benchmark for Evaluating Structured Output Quality in Large Language Models
- The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
- AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
- ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
- StruQ: Defending Against Prompt Injection with Structured Queries
- SecAlign: Defending Against Prompt Injection with Preference Optimization
- Defeating Prompt Injections by Design
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