The Causal Impact of Tool Affordance on Safety Alignment in LLM Agents

Large language models (LLMs) are increasingly deployed as agents with access to executable tools, enabling direct interaction with external systems. However, most safety evaluations remain text-centric and assume that compliant language implies safe behavior. In this work, we empirically examine how executable tool affordance affects safety alignment in LLM agents using a paired evaluation framework that compares text-only chatbot behavior with tool-enabled agent behavior under identical prompts and policies. Experiments are conducted in a deterministic financial transaction environment with binary safety constraints across 1,500 evaluation instances. To separate intent from outcome, we distinguish between attempted and realized violations using dual enforcement regimes that block or permit unsafe actions. Both evaluated models maintain perfect compliance in text-only settings, yet exhibit sharp increases in violations after tool access is introduced, reaching rates up to 85% despite unchanged rules. We observe substantial gaps between attempted and executed violations, indicating that external guardrails can suppress visible harm while masking persistent misalignment. Agents also develop spontaneous constraint circumvention without adversarial prompting. These results show that tool affordance can act as a significant driver of safety misalignment under controlled conditions and that text-based evaluation alone may be insufficient for assessing agentic systems.