← Back to all news
arXiv Computation and Language By Adel Khorramrouz, Yi-Chia Wang, Sharon Levy

Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs

Read the original on arXiv Computation and Language →

The paper "Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs" presents a multi‑agent framework that uses Moral Foundations Theory to reframe harmful prompts in a way that aligns with different moral foundations, thereby enabling large language models to bypass safety guardrails. The authors demonstrate that this approach achieves high single‑trial jailbreak rates across several open‑source and proprietary models, with success increasing under repeated attempts. Their analysis also reveals that existing safety benchmarks are unevenly distributed across moral foundations, focusing on those foundations where models already show biases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

Hugging Face Trending Papers
Aug 27

How Language Models Organize and Structure Moral Knowledge

The paper investigates how large language models encode moral knowledge by training linear probes for each Moral Foundations Theory category and analyzing their geometric relationships. It finds that the model’s moral directions are largely independent yet share a common component, indicating integration rather than collapse into a single detector. This structure is consistent across architectures, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of Moral Foundations Theory.

llms
More like this →
arXiv AI
Sep 4

Representational alignment yields generalizable safety in language models

The paper argues that aligning large language models (LLMs) at the level of latent representations—specifically by matching their internal categorization of moral concepts to human prototype-based judgments—improves safety. Current alignment methods that focus on observable responses fail to preserve fine-grained moral categorization, leaving models vulnerable to adversarial rephrasings. By optimizing representational similarity, the authors demonstrate that LLMs can maintain more robust moral categorization and exhibit better adversarial robustness across multiple benchmarks and model sizes.

By Lingyu Li, Yan Teng, Yingchun Wang, Xia Hu
llmsbenchmarkssafety
More like this →
arXiv Machine Learning
Jun 5

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

arXiv:2605. 03217v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model outputs as simply "biased" or "unbiased.

By Yash Aggarwal, Atmika Gorti, Vinija Jain, Aman Chadha, Krishnaprasad Thirunarayan, Manas Gaur
llmsefficiencysafety
More like this →
arXiv AI
Aug 28

How Language Models Organize and Structure Moral Knowledge

The study investigates how large language models encode moral knowledge by training linear probes for each category of Moral Foundations Theory. It finds that the model’s representations for different moral foundations occupy distinct, largely independent dimensions yet share a common positive component, indicating an integrated but nuanced moral structure. This geometry is consistent across model architectures and scales, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of the theory.

By Orion Reblitz-Richardson
llms
More like this →