Bottom line: Alignment techniques for LLMs can technically be used for both safety and censorship and require stronger transparency and oversight.
Researchers at Ludwig Maximilian University of Munich identify significant potential for misuse in AI security mechanisms for value alignment of language models. Alignment procedures could be instrumentalised by actors as tools for information control.
Techniques for value alignment of large language models (Value Alignment, RLHF and related procedures) aim to make models safer and more compliant. At the same time, LMU research shows that these mechanisms are structurally designed in a way that allows them to be used for censorship or selective information control.
For CIOs and CTOs, this means: The security measures employed in language models for enterprise deployments require differentiated assessment. It is not sufficient to accept manufacturer guarantees for “safe” alignment practices — the technical basis of these procedures must be disclosed more transparently and be verifiable in order to rule out misuse through centralised control.
The LMU warning underscores the tension between technical security and informational autonomy: While alignment techniques address genuine risks such as toxic outputs or leakage of training data, they can also be used as a vehicle for unjustified restrictions. This is relevant for the governance of AI systems in regulatory contexts such as the EU AI Act, where “safety” and “compliance” must be defined without resorting to technical control mechanisms that ultimately enable power concentration.
Source: www.golem.de · Published 15 July 2026
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