Defense Mechanisms for Securing LLM-based Evaluation Pipelines

Design defense mechanisms that secure LLM-as-a-Judge evaluation pipelines against adversarial manipulation throughout the assessment process.

Background

Even when adversarial examples are detected, end-to-end evaluation pipelines may remain exposed if downstream components or aggregation steps can still be influenced.

The paper emphasizes the need for comprehensive, pipeline-level defenses that address threats at multiple stages, ensuring secure and trustworthy automated evaluation.

References

The open research problems in this context are: Design defence mechanisms for secured evaluation pipelines.

— Security in LLM-as-a-Judge: A Comprehensive SoK  (2603.29403 - Masoud et al., 31 Mar 2026) in Section 7.1, Vulnerability to Adversarial Prompt Manipulation (Challenges and Open Problems)

We leave coordinated multi-surface manipulation, compromised provenance anchors or tools, and alternative response policies to future work.

— PIPES: Securing Agent Perception with Provenance and Priors  (2608.12789 - Kariyappa et al., 13 Aug 2026) in Section 7, Limitations