Dorian Quelle, Lisa-Maria Neudert, Jonathan Bright +1cs.AI
In this paper we present an active, constantly updated AI benchmark which measures the integrity of frontier language models against being co-opted for state-backed information operations. We draw on over 2,100 information operations from a live monitoring pipeline which tracks Russian, Chinese and Iranian state-backed information assets. Alongside this paper, we release a companion website that tracks the most prominent claims spread by state-backed media outlets, updated weekly, available from: pattrn.ai/research/infoopsbench. The dynamic nature of the benchmark makes it resistant to saturation. In the benchmark, we test 17 models from 8 providers across four prompt framings. We find that most models can be co-opted for information operations. Integrity scores, defined as the percentage of refused requests, range from 8.8% to 94.5%, an 85.7-percentage-point spread not explained by model size. Model choice also changes the character of the resulting operation. Some models fabricate details and produce output more harmful than the source material, others defuse claims even while complying, and fact-checking rates vary from 2.9% to 72.9%. Integrity against information operations is at least partly related to refusal to produce content even for benign claims, illustrating the challenge of balancing model usability with safety. With one exception (Z.ai's GLM 5.2), the Chinese-developed models sharply cut compliance on factually grounded but China-critical claims, dropping 48-70 percentage points relative to matched benign claims.
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO user behavior, while existing unsupervised approaches rely on oversimplified assumptions of coordination among IO users that may not exist in practice. To overcome the limitations of existing methods, we formulate IO user detection as an anomaly detection problem and propose a novel unsupervised IO user detection approach called Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR), which leverages multimodal data, including temporal online user behavior, such as message posting activities, and the textual content of the messages. The motivation is that IO users are typically a very small fraction of all online users and have unique temporal behavioral and language patterns. Specifically, we train a Temporal Point Process (TPP) to capture abnormal temporal behavioral patterns of IO users because they are known to behave in a coordinated manner for IO campaigns. We further introduce a novel evidence function that converts LLM responses, which are generated from user post timelines, into quantitative scores to adjust the TPP outputs for better IO user detection. Experimental results show that TENSOR outperforms the baselines on five real-world IO datasets. Code is available at https://github.com/xiuzhenzhang/TENSOR.