AUTOSIGMA, an automated solution for transforming unstructured CTI reports into relevant Sigma rules that enables accurate, context-aware, and relevant rule generation, outperforms alternative solutions and LLM models in rule validity, rule relevancy, MITRE ATT&CK technique coverage, and robustness to input quality.
Sepehr Ghaffarzadegan, Boubakr Nour, M. Pourzandi et al.· 0 citations
DAEI is proposed, a denoising-aware embedding inversion pipeline that combines a residual denoising autoencoder with generative text inversion where the denoiser is trained in an unsupervised manner using Stein's unbiased risk estimate to enable denoising from noisy observations alone.
Yubo Wang, Shujie Cui, James Bailey et al.· 0 citations
Learned restraint through least-privilege aware post-training is useful as an additional control layer for tool-using agents in executable terminal and MCP environments, but it does not replace permission gates and sandboxing.
This work examines emoji-augmented prompts as a test case for robustness, evaluating 50 prompts across four open-source LLMs, showing substantial variation in robustness.
M. Gopinadh· 0 citations
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Abliteration, the removal of refusal capabilities from large language models by projecting weight matrices orthogonal to an extracted refusal direction, has emerged as a prominent safety concern through its ability to bypass post-training alignment using only a small set of contrastive prompts. We find that existing de...
Verifiable Latent Alignments (VLA), an activation-aware framework for monitoring and steering these private communication channels, is introduced and shows that the evaluated private channel attacks can be monitored without training the primary monitor on attack examples and mitigated when matched counterfactual access...
Ramneet Kaur, Pradyumna Chari, Ramesh Raskar et al.· 1 citation
It is argued that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and CTIFoundry, an agent-native corpus scaffold, is presented.
Yutong Cheng, Chang-Ze Li, Q. Cui et al.· 0 citations
The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sectors. While proprietary solutions dominate commercial applications, a rapidly growing ecosystem of open-source Optical Character Recognition (OCR) engines, Large Language Models (LLM...
Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (AI) in general is to ensure privacy when using them, meaning that personally identifiable information (PII) is removed from any text that enters an LLM. These challenge...
