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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2601.18552 |
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| _version_ | 1866918305790427136 |
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| author | Srivastav, Devansh Pape, David Schönherr, Lea |
| author_facet | Srivastav, Devansh Pape, David Schönherr, Lea |
| contents | LLMs are increasingly embedded in everyday decision-making, yet their outputs can encode subtle, unintended behaviours that shape user beliefs and actions. We refer to these covert, goal-directed behaviours as hidden intentions, which may arise from training and optimisation artefacts, or be deliberately induced by an adversarial developer, yet remain difficult to detect in practice. We introduce a taxonomy of ten categories of hidden intentions, grounded in social science research and organised by intent, mechanism, context, and impact, shifting attention from surface-level behaviours to design-level strategies of influence. We show how hidden intentions can be easily induced in controlled models, providing both testbeds for evaluation and demonstrations of potential misuse. We systematically assess detection methods, including reasoning and non-reasoning LLM judges, and find that detection collapses in realistic open-world settings, particularly under low-prevalence conditions, where false positives overwhelm precision and false negatives conceal true risks. Stress tests on precision-prevalence and precision-FNR trade-offs reveal why auditing fails without vanishingly small false positive rates or strong priors on manipulation types. Finally, a qualitative case study shows that all ten categories manifest in deployed, state-of-the-art LLMs, emphasising the urgent need for robust frameworks. Our work provides the first systematic analysis of detectability failures of hidden intentions in LLMs under open-world settings, offering a foundation for understanding, inducing, and stress-testing such behaviours, and establishing a flexible taxonomy for anticipating evolving threats and informing governance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18552 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unknown Unknowns: Why Hidden Intentions in LLMs Evade Detection Srivastav, Devansh Pape, David Schönherr, Lea Computation and Language Machine Learning LLMs are increasingly embedded in everyday decision-making, yet their outputs can encode subtle, unintended behaviours that shape user beliefs and actions. We refer to these covert, goal-directed behaviours as hidden intentions, which may arise from training and optimisation artefacts, or be deliberately induced by an adversarial developer, yet remain difficult to detect in practice. We introduce a taxonomy of ten categories of hidden intentions, grounded in social science research and organised by intent, mechanism, context, and impact, shifting attention from surface-level behaviours to design-level strategies of influence. We show how hidden intentions can be easily induced in controlled models, providing both testbeds for evaluation and demonstrations of potential misuse. We systematically assess detection methods, including reasoning and non-reasoning LLM judges, and find that detection collapses in realistic open-world settings, particularly under low-prevalence conditions, where false positives overwhelm precision and false negatives conceal true risks. Stress tests on precision-prevalence and precision-FNR trade-offs reveal why auditing fails without vanishingly small false positive rates or strong priors on manipulation types. Finally, a qualitative case study shows that all ten categories manifest in deployed, state-of-the-art LLMs, emphasising the urgent need for robust frameworks. Our work provides the first systematic analysis of detectability failures of hidden intentions in LLMs under open-world settings, offering a foundation for understanding, inducing, and stress-testing such behaviours, and establishing a flexible taxonomy for anticipating evolving threats and informing governance. |
| title | Unknown Unknowns: Why Hidden Intentions in LLMs Evade Detection |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.18552 |