LLM Research
A new theoretical framework formalizes how large language models process, weight, and become susceptible to misleading information — with implications for AI safety, adversarial attacks, and digital authenticity.
Adversarial Attacks
A new theoretical framework unifies adversarial vulnerability in neural networks with LLM hallucination, proposing that both arise from a fundamental uncertainty trade-off in learned representations.
AI Security
New research reveals how adversarial attacks can manipulate AI explanation systems to mislead human decision-makers, with critical implications for content authenticity verification.
LLM Security
Researchers detail how prompt injection, jailbreaking, and gradient-based attacks systematically defeat the layered safety mechanisms designed to keep large language models aligned and secure.
AI Security
Researchers reveal how imperceptible visual perturbations embedded in images can hijack vision-language models, bypassing safety filters and manipulating AI outputs without human detection.
LLM Security
New research reveals how adversarial control tokens can manipulate LLM-as-a-Judge systems into completely reversing their binary decisions, exposing critical vulnerabilities in AI evaluation pipelines.
AI Security
New research demonstrates how multiple LLMs working together can generate adaptive adversarial attacks that bypass AI safety filters. The technique uses collaborative reasoning to craft prompts that exploit model vulnerabilities more effectively than single-agent approaches.
AI Detection
New research introduces PADBen, a comprehensive benchmark evaluating how AI text detectors perform against paraphrase attacks. The framework reveals critical vulnerabilities in current detection systems through adversarial testing.