arXiv AI
Shielded Analysis: Certification and Characterization of Defensibility in Systems under Adversarial Interaction
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 12
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
arXiv:2606. 13621v1 Announce Type: new Abstract: Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions.
Hugging Face Trending Papers
Jun 11
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the wrong product.
arXiv Machine Learning
Jun 25
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
arXiv AI
Sep 2
Defense-as-Skill: Evolving Runtime Guard Skill for Skill-Augmented Agents
arXiv:2609.01487v1 Announce Type: cross Abstract: Skill-augmented agents load reusable skills as persistent runtime context, improving task performance but also giving malicious skills a durable chan...
arXiv Machine Learning
Jul 21
A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic
arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.
arXiv AI
Aug 6
Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning
arXiv:2608. 05045v1 Announce Type: cross Abstract: Released aligned large language models remain vulnerable to malicious downstream finetuning.