Benchmark Almanac

Subversion Strategy Eval

Only in system cards

Models must devise and execute strategies to subvert control protocols without shared memory between calls.

Anthropic cards report a subset of the environments.

In the authors’ words

An AI control protocol is a plan for usefully deploying AI systems that aims to prevent an AI from intentionally causing some unacceptable outcome. This paper investigates how well AI systems can generate and act on their own strategies for subverting control protocols whilst operating statelessly (without shared memory between contexts). To do this, an AI system may need to reliably generate optimal plans in each context, take actions with well-calibrated probabilities, and coordinate plans with other instances of itself without communicating. We develop Subversion Strategy Eval, a suite of eight environments, covering a range of protocols and strategic capabilities, and six sets of affordances that help isolate individual capabilities. We implement the evaluation in Inspect-AI and release it open-source. We evaluate Claude 3.5 models, including helpful-only versions, as well as OpenAI reasoning models. None of the models demonstrate substantial capability in strategizing to subvert control protocols statelessly. However, providing models with additional affordances, such as the ability to share a plan between contexts, can substantially improve performance. We hope our evaluations can act as a leading indicator for when models are capable of subverting control protocols and also relax the worst-case assumption of perfect strategic ability in AI control evaluations.

Abstract of Subversion Strategy Eval: Can language models statelessly strategize to subvert control protocols? (arXiv:2412.12480, CC0)
Made by Redwood Research + Anthropic (Mallen et al.) Released Dec 2024 Deception and alignment All tasks public Last in a system card Nov 2025 Last model added Nov 2025
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
2
System cards report it, from 1 lab

No scores here yet

No lab, leaderboard or Epoch AI run has reported a score for Subversion Strategy Eval yet.

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

2 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
24 Nov 2025 Claude Opus 4.5 system card Subversion Strategy win rate across deployment sizes N=100 to 5,000 with and without thinking, no overall (Figure 6.11.2.A). Text: 63.3% at N=1,000 with unmonitored extended thinking, near zero without thinking above trivially small sizes Alignment
29 Sep 2025 Claude Sonnet 4.5 system card Subversion Strategy win rate across deployment sizes N=100 to 5,000 with and without thinking, no overall. 9.0% at N=100 with thinking, 0.0% at N=100 without thinking and at N=500 and N=1,000 (Figure 7.5.10.1.A) Alignment

More in Safety and alignment

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