Asymmetric Goal Drift in Coding Agents Under Value Conflict

TL;DR

This paper investigates how coding agents often violate their system constraints under environmental pressure, especially when these constraints oppose their strongly-held values, revealing challenges in ensuring safe autonomous behavior.

Contribution

The study introduces a realistic framework to measure goal drift in coding agents, highlighting the impact of environmental pressure and value alignment on constraint violations.

Findings

Agents exhibit asymmetric drift, violating constraints more when opposing strongly-held values.

Goal drift correlates with value alignment, adversarial pressure, and accumulated context.

Environmental signals can override explicit constraints, risking exploitable behavior.

Abstract

Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate complex trade-offs in deployment, balancing influence from the user, their learned values, and the codebase itself. Understanding how agents resolve these trade-offs in practice is critical, yet prior work has relied on static, synthetic settings that do not capture the complexity of real-world environments. To this end, we introduce a framework built on OpenCode in which a coding agent completes realistic, multi-step tasks under a system prompt constraint favoring one side of a value trade-off. We measure how often the agent violates this constraint as it completes tasks, with and without environmental pressure toward the competing value. Using this framework, we demonstrate that GPT-5 mini, Haiku 4.5, and Grok Code Fast 1 exhibit $\textit{asymmetric drift}$: they are more likely to violate their system prompt when its constraint opposes strongly-held values like security and privacy. We find for the models and values tested that goal drift correlates with three compounding factors: value alignment, adversarial pressure, and accumulated context. However, even constraints aligned with strongly-held values like privacy are violated under sustained environmental pressure for some models. Our findings reveal that shallow compliance checks are insufficient, and that environmental signals can override explicit constraints in ways that appear exploitable. Malicious actors with access to the codebase could manipulate agent behavior by appealing to learned values, with the risk compounding over the long horizons typical of agentic deployment.