Papers
1–3 of 3Research Paper·Feb 5, 2026·B2BConsumer
Learning to Inject: Automated Prompt Injection via Reinforcement Learning
Prompt injection is one of the most critical vulnerabilities in LLM agents; yet, effective automated attacks remain largely unexplored from an optimization perspective. Existing methods heavily depend...
6.0 viability
Research Paper·Feb 10, 2026
Not-in-Perspective: Towards Shielding Google's Perspective API Against Adversarial Negation Attacks
The rise of cyberbullying in social media platforms involving toxic comments has escalated the need for effective ways to monitor and moderate online interactions. Existing solutions of automated toxi...
6.0 viability
Research Paper·Jan 23, 2026
LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems
Automated fact-checking (AFC) systems are susceptible to adversarial attacks, enabling false claims to evade detection. Existing adversarial frameworks typically rely on injecting noise or altering se...
5.0 viability