SALLIE: Safeguarding Against Latent Language & Image Exploits
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911574281682944 |
|---|---|
| author | Azov, Guy Rivlin, Ofer Shtar, Guy |
| author_facet | Azov, Guy Rivlin, Ofer Shtar, Guy |
| contents | Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections (arXiv:2307.15043, Greshake et al., 2023, arXiv:2306.13213). Existing defenses often degrade performance through complex input transformations or treat multimodal threats as isolated problems (arXiv:2309.00614, arXiv:2310.03684, Zhang et al., 2025). To address the critical gap for a unified, modal-agnostic defense that mitigates both textual and visual threats simultaneously without degrading performance or requiring architectural modifications, we introduce SALLIE (Safeguarding Against Latent Language & Image Exploits), a lightweight runtime detection framework rooted in mechanistic interpretability (Lindsey et al., 2025, Ameisen et al., 2025). By integrating seamlessly into standard token-level fusion pipelines (arXiv:2306.13549), SALLIE extracts robust signals directly from the model's internal activations. At inference, SALLIE defends via a three-stage architecture: (1) extracting internal residual stream activations, (2) calculating layer-wise maliciousness scores using a K-Nearest Neighbors (k-NN) classifier, and (3) aggregating these predictions via a layer ensemble module. We evaluate SALLIE on compact, open-source architectures - Phi-3.5-vision-instruct (arXiv:2404.14219), SmolVLM2-2.2B-Instruct (arXiv:2504.05299), and gemma-3-4b-it (arXiv:2503.19786) - prioritized for practical inference times and real-world deployment costs. Our comprehensive evaluation pipeline spans over ten datasets and more than five strong baseline methods from the literature, and SALLIE consistently outperforms these baselines across a wide range of experimental settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SALLIE: Safeguarding Against Latent Language & Image Exploits Azov, Guy Rivlin, Ofer Shtar, Guy Cryptography and Security Artificial Intelligence I.2.7; I.2.10; K.6.5; I.2.6 Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections (arXiv:2307.15043, Greshake et al., 2023, arXiv:2306.13213). Existing defenses often degrade performance through complex input transformations or treat multimodal threats as isolated problems (arXiv:2309.00614, arXiv:2310.03684, Zhang et al., 2025). To address the critical gap for a unified, modal-agnostic defense that mitigates both textual and visual threats simultaneously without degrading performance or requiring architectural modifications, we introduce SALLIE (Safeguarding Against Latent Language & Image Exploits), a lightweight runtime detection framework rooted in mechanistic interpretability (Lindsey et al., 2025, Ameisen et al., 2025). By integrating seamlessly into standard token-level fusion pipelines (arXiv:2306.13549), SALLIE extracts robust signals directly from the model's internal activations. At inference, SALLIE defends via a three-stage architecture: (1) extracting internal residual stream activations, (2) calculating layer-wise maliciousness scores using a K-Nearest Neighbors (k-NN) classifier, and (3) aggregating these predictions via a layer ensemble module. We evaluate SALLIE on compact, open-source architectures - Phi-3.5-vision-instruct (arXiv:2404.14219), SmolVLM2-2.2B-Instruct (arXiv:2504.05299), and gemma-3-4b-it (arXiv:2503.19786) - prioritized for practical inference times and real-world deployment costs. Our comprehensive evaluation pipeline spans over ten datasets and more than five strong baseline methods from the literature, and SALLIE consistently outperforms these baselines across a wide range of experimental settings. |
| title | SALLIE: Safeguarding Against Latent Language & Image Exploits |
| topic | Cryptography and Security Artificial Intelligence I.2.7; I.2.10; K.6.5; I.2.6 |
| url | https://arxiv.org/abs/2604.06247 |