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Found 12 papers, 10 papers with code
AmpleGCG-Plus: A Strong Generative Model of Adversarial Suffixes to Jailbreak LLMs with Higher Success Rates in Fewer Attempts
Notably, AmpleGCG-Plus jailbreaks the newer GPT-4o series of models at similar rates to GPT-4, and, uncovers vulnerabilities against the recently proposed circuit breakers defense.
AdvWeb: Controllable Black-box Attacks on VLM-powered Web Agents
Vision Language Models (VLMs) have revolutionized the creation of generalist web agents, empowering them to autonomously complete diverse tasks on real-world websites, thereby boosting human efficiency and productivity.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery.
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage
In this work, we narrow this gap by conducting the first study on the privacy risks of generalist web agents in adversarial environments.
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
Aligning human preference and value is an important requirement for building contemporary foundation models and embodied AI.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs
Moreover, we utilize those successful suffixes as training data to learn a generative model, named AmpleGCG, which captures the distribution of adversarial suffixes given a harmful query and enables the rapid generation of hundreds of suffixes for any harmful queries in seconds.
AttributionBench: How Hard is Automatic Attribution Evaluation?
Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence.
A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents
There is a surprisingly large gap between the speed and scale of their development and deployment and our understanding of their safety risks.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
To effectively use large language models (LLMs) for real-world queries, it is imperative that they generalize to the long-tail distribution, i. e. rare examples where models exhibit low confidence.
ChatCounselor: A Large Language Models for Mental Health Support
This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support.
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning
Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language.
