ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases
The tendency to find and exploit "shortcuts" to complete tasks poses
significant risks for reliable assessment and deployment of large language
models (LLMs). For example, an LLM agent with access to unit tests may delete
failing tests rather than fix the underlying bug. Such behavior undermines both
the validity of benchmark results and the reliability of real-world LLM coding
assistant deployments.
  To quantify, study, and mitigate such behavior, we introduce ImpossibleBench,
a benchmark framework that systematically measures LLM agents' propensity to
exploit test cases. ImpossibleBench creates "impossible" variants of tasks from
existing benchmarks like LiveCodeBench and SWE-bench by introducing direct
conflicts between the natural-language specification and the unit tests. We
measure an agent's "cheating rate" as its pass rate on these impossible tasks,
where any pass necessarily implies a specification-violating shortcut.
  As a practical framework, ImpossibleBench is not just an evaluation but a
versatile tool. We demonstrate its utility for: (1) studying model behaviors,
revealing more fine-grained details of cheating behaviors from simple test
modification to complex operator overloading; (2) context engineering, showing
how prompt, test access and feedback loop affect cheating rates; and (3)
developing monitoring tools, providing a testbed with verified deceptive
solutions. We hope ImpossibleBench serves as a useful framework for building
more robust and reliable LLM systems.
  Our implementation can be found at
https://github.com/safety-research/impossiblebench.
68faded5f158a71c5a2f583b
Ziqian Zhong
claimed_verified
2025-10-24T16:11:30.027Z
62c0a2e8564b51e080d64af8
/avatars/7ffed6712ead59919832ec71c0e3f5d1.svg
Ziqian Zhong
user
fjzzq2002
68faded5f158a71c5a2f583c
Aditi Raghunathan
68faded5f158a71c5a2f583d
Nicholas Carlini
ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases
The tendency to find and exploit "shortcuts" to complete tasks poses
significant risks for reliable assessment and deployment of large language
models (LLMs). For example, an LLM agent with access to unit tests may delete
failing tests rather than fix the underlying bug. Such behavior undermines both
the validity of benchmark results and the reliability of real-world LLM coding
assistant deployments.
  To quantify, study, and mitigate such behavior, we introduce ImpossibleBench,
a benchmark framework that systematically measures LLM agents' propensity to
exploit test cases. ImpossibleBench creates "impossible" variants of tasks from
existing benchmarks like LiveCodeBench and SWE-bench by introducing direct
conflicts between the natural-language specification and the unit tests. We
measure an agent's "cheating rate" as its pass rate on these impossible tasks,
where any pass necessarily implies a specification-violating shortcut.
  As a practical framework, ImpossibleBench is not just an evaluation but a
versatile tool. We demonstrate its utility for: (1) studying model behaviors,
revealing more fine-grained details of cheating behaviors from simple test
modification to complex operator overloading; (2) context engineering, showing
how prompt, test access and feedback loop affect cheating rates; and (3)
developing monitoring tools, providing a testbed with verified deceptive
solutions. We hope ImpossibleBench serves as a useful framework for building
more robust and reliable LLM systems.
  Our implementation can be found at
https://github.com/safety-research/impossiblebench.
ImpossibleBench is a benchmark framework that measures and mitigates LLMs' tendency to exploit test cases by introducing impossible task variants, thereby enhancing model reliability.
large language models LLMs unit tests benchmark framework ImpossibleBench LiveCodeBench SWE-bench cheating rate specification-violating shortcuts context engineering monitoring tools