| _version_ | 1866901796433166336 |
|---|---|
| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | The rapid proliferation of large-scale foundation models has ushered in unprecedented capabilities across various domains, yet concurrently introduced significant safety and security challenges. A primary concern is the phenomenon of "jailbreaking," where malicious or clever prompts circumvent safety filters, eliciting harmful, unethical, or otherwise undesirable responses from the models. Current mitigation strategies largely rely on reactive patching – identifying new jailbreak techniques and subsequently updating safety layers or fine-tuning models. This paper argues for a paradigm shift from this reactive, external patching approach to architecting intrinsic jailbreak resistance directly into the core design and training of foundation models. We propose a conceptual framework centered on multi-faceted architectural interventions, including constitutional pre-training, adversarial safety alignment during foundational training, explicit layered safety modules within the model's inference path, and a more robust integration of explainable AI for proactive vulnerability identification. By embedding safety as a first-class design principle, rather than an afterthought, we aim to foster models that are inherently more robust against known and novel adversarial attacks, thereby enhancing their trustworthiness, reliability, and societal benefit. This work outlines a path towards more resilient AI systems, moving beyond a perpetual game of cat-and-mouse to cultivate truly secure and beneficial artificial general intelligence. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17827825 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Patching to Preventing: Architecting Intrinsic Jailbreak Resistance in Foundation Models Revista, Zen IA, 10 The rapid proliferation of large-scale foundation models has ushered in unprecedented capabilities across various domains, yet concurrently introduced significant safety and security challenges. A primary concern is the phenomenon of "jailbreaking," where malicious or clever prompts circumvent safety filters, eliciting harmful, unethical, or otherwise undesirable responses from the models. Current mitigation strategies largely rely on reactive patching – identifying new jailbreak techniques and subsequently updating safety layers or fine-tuning models. This paper argues for a paradigm shift from this reactive, external patching approach to architecting intrinsic jailbreak resistance directly into the core design and training of foundation models. We propose a conceptual framework centered on multi-faceted architectural interventions, including constitutional pre-training, adversarial safety alignment during foundational training, explicit layered safety modules within the model's inference path, and a more robust integration of explainable AI for proactive vulnerability identification. By embedding safety as a first-class design principle, rather than an afterthought, we aim to foster models that are inherently more robust against known and novel adversarial attacks, thereby enhancing their trustworthiness, reliability, and societal benefit. This work outlines a path towards more resilient AI systems, moving beyond a perpetual game of cat-and-mouse to cultivate truly secure and beneficial artificial general intelligence. |
| title | From Patching to Preventing: Architecting Intrinsic Jailbreak Resistance in Foundation Models |
| url | https://doi.org/10.5281/zenodo.17827825 |