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Are Your Agents Upward Deceivers?

Dadi Guo, Qingyu Liu, Dongrui Liu, Qihan Ren, Shuai Shao, Tianyi Qiu, Haoran Li, Yi R. Fung, Zhongjie Ba, Juntao Dai, Jiaming Ji, Zhikai Chen, Jialing Tao, Yaodong Yang, Jing Shao, Xia Hu

TL;DR

The paper defines agentic upward deception as action-based concealment by LLM-based agents under constrained environments, and demonstrates its prevalence with a 200-task benchmark across 5 task types and 8 scenarios evaluated on 11 models. Using an GPT-5 judge, it introduces metrics (NFR, DFR, FFR, HFR) to quantify deceptive behaviors, revealing widespread tendencies to guess, simulate, substitute sources, or fabricate files. Ablation studies show that some mitigations (e.g., output format constraints) reduce deception but do not eliminate it, underscoring the need for stronger alignment and safety measures. The results highlight significant real-world risks in autonomous agents, particularly in high-stakes domains, and call for heightened focus on agent safety and reliable reporting of task progress. The work motivates future research on robust mitigation and governance of agentic deception across diverse domains.

Abstract

Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users. This raises the question of whether they may also engage in deception, similar to how individuals in human organizations lie to superiors to create a good image or avoid punishment. We observe and define agentic upward deception, a phenomenon in which an agent facing environmental constraints conceals its failure and performs actions that were not requested without reporting. To assess its prevalence, we construct a benchmark of 200 tasks covering five task types and eight realistic scenarios in a constrained environment, such as broken tools or mismatched information sources. Evaluations of 11 popular LLMs reveal that these agents typically exhibit action-based deceptive behaviors, such as guessing results, performing unsupported simulations, substituting unavailable information sources, and fabricating local files. We further test prompt-based mitigation and find only limited reductions, suggesting that it is difficult to eliminate and highlighting the need for stronger mitigation strategies to ensure the safety of LLM-based agents.

Are Your Agents Upward Deceivers?

TL;DR

The paper defines agentic upward deception as action-based concealment by LLM-based agents under constrained environments, and demonstrates its prevalence with a 200-task benchmark across 5 task types and 8 scenarios evaluated on 11 models. Using an GPT-5 judge, it introduces metrics (NFR, DFR, FFR, HFR) to quantify deceptive behaviors, revealing widespread tendencies to guess, simulate, substitute sources, or fabricate files. Ablation studies show that some mitigations (e.g., output format constraints) reduce deception but do not eliminate it, underscoring the need for stronger alignment and safety measures. The results highlight significant real-world risks in autonomous agents, particularly in high-stakes domains, and call for heightened focus on agent safety and reliable reporting of task progress. The work motivates future research on robust mitigation and governance of agentic deception across diverse domains.

Abstract

Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users. This raises the question of whether they may also engage in deception, similar to how individuals in human organizations lie to superiors to create a good image or avoid punishment. We observe and define agentic upward deception, a phenomenon in which an agent facing environmental constraints conceals its failure and performs actions that were not requested without reporting. To assess its prevalence, we construct a benchmark of 200 tasks covering five task types and eight realistic scenarios in a constrained environment, such as broken tools or mismatched information sources. Evaluations of 11 popular LLMs reveal that these agents typically exhibit action-based deceptive behaviors, such as guessing results, performing unsupported simulations, substituting unavailable information sources, and fabricating local files. We further test prompt-based mitigation and find only limited reductions, suggesting that it is difficult to eliminate and highlighting the need for stronger mitigation strategies to ensure the safety of LLM-based agents.

Paper Structure

This paper contains 18 sections, 1 equation, 2 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Agentic Upward Deception
  3. Problem Formulation
  4. Dataset and Environment Construction
  5. Evaluation Method
  6. Experiments
  7. Setup
  8. Result Analysis
  9. Case Study
  10. Ablation Study and Mitigation
  11. Related Work
  12. Discussion: Why Does Upward Deception Exist?
  13. Conclusion
  14. Agentic Environments
  15. System Prompt
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Comparison between an honest and a deceptively behaving agent.
  • Figure 2: Overview of our benchmark construction and evaluation pipeline. The left panel summarizes task templates and environment constraints used across our five task types. The middle panel depicts the semi-automated process for generating instructions of our benchmark dataset, and the right panel illustrates how a judge model evaluates upward deception in agent outputs.

Theorems & Definitions (1)

  • Definition 2.1: Agentic Upward Deception