Diffusion Language Models (DLMs) are emerging as competitive alternatives to auto-regressive models, but their flexible, any-order decoding may introduce new trustworthiness risks. We present TrustDLM, a comprehensive benchmark for evaluating DLM trustworthiness across safety, privacy, and fairness with multiple categories of static post contexts. Our results show that while DLMs are generally trustworthy with user prompts alone, their alignment degrades noticeably when malicious post-contexts are attached to masked responses. We further find that decoding order and generation length affect evaluation outcomes, while longer contexts do not necessarily induce stronger effects. Finally, we introduce TrustDLM-Auto, the first automatic framework for identifying vulnerable DLM configurations, which reveals substantial weaknesses across models and dimensions. Overall, TrustDLM provides the first trustworthiness evaluation framework specifically designed for DLMs, with practical applicability further validated on closed-source DLMs. Our work will help the community enhance the trustworthiness of DLMs. Our code is available at https://github.com/PKU-ML/TrustDLM.