Appears in the 23rd International Conference on Autonomous Agents and
Multi-Agent Systems (AAMAS),...
We consider the obnoxious facility location problem (in which agents prefer
the facility location to be far from them) and propose a hierarchy of
distance-based proportional fairness concepts for the problem. These fairness
axioms ensure that groups of agents at the same location are guaranteed to be a
distance from the facility proportional to their group size. We consider
deterministic and randomized mechanisms, and compute tight bounds on the price
of proportional fairness. In the deterministic setting, we show that our
proportional fairness axioms are incompatible with strategyproofness, and prove
asymptotically tight ϵ-price of anarchy and stability bounds for
proportionally fair welfare-optimal mechanisms. In the randomized setting, we
identify proportionally fair and strategyproof mechanisms that give an expected
welfare within a constant factor of the optimal welfare. Finally, we prove
existence results for two extensions to our model.Abstract
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.
However, despite their remarkable capabilities, the safety and security of
these agents against malicious attacks remain critically underexplored, raising
significant concerns about their safe deployment. To uncover and exploit such
vulnerabilities in web agents, we provide AdvWeb, a novel black-box attack
framework designed against web agents. AdvWeb trains an adversarial prompter
model that generates and injects adversarial prompts into web pages, misleading
web agents into executing targeted adversarial actions such as inappropriate
stock purchases or incorrect bank transactions, actions that could lead to
severe real-world consequences. With only black-box access to the web agent, we
train and optimize the adversarial prompter model using DPO, leveraging both
successful and failed attack strings against the target agent. Unlike prior
approaches, our adversarial string injection maintains stealth and control: (1)
the appearance of the website remains unchanged before and after the attack,
making it nearly impossible for users to detect tampering, and (2) attackers
can modify specific substrings within the generated adversarial string to
seamlessly change the attack objective (e.g., purchasing stocks from a
different company), enhancing attack flexibility and efficiency. We conduct
extensive evaluations, demonstrating that AdvWeb achieves high success rates in
attacking SOTA GPT-4V-based VLM agent across various web tasks. Our findings
expose critical vulnerabilities in current LLM/VLM-based agents, emphasizing
the urgent need for developing more reliable web agents and effective defenses.
Our code and data are available at https://ai-secure.github.io/AdvWeb/ .Abstract
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy
Leakage
Generalist web agents have evolved rapidly and demonstrated remarkable
potential. However, there are unprecedented safety risks associated with these
them, which are nearly unexplored so far. In this work, we aim to narrow this
gap by conducting the first study on the privacy risks of generalist web agents
in adversarial environments. First, we present a threat model that discusses
the adversarial targets, constraints, and attack scenarios. Particularly, we
consider two types of adversarial targets: stealing users' specific personally
identifiable information (PII) or stealing the entire user request. To achieve
these objectives, we propose a novel attack method, termed Environmental
Injection Attack (EIA). This attack injects malicious content designed to adapt
well to different environments where the agents operate, causing them to
perform unintended actions. This work instantiates EIA specifically for the
privacy scenario. It inserts malicious web elements alongside persuasive
instructions that mislead web agents into leaking private information, and can
further leverage CSS and JavaScript features to remain stealthy. We collect 177
actions steps that involve diverse PII categories on realistic websites from
the Mind2Web dataset, and conduct extensive experiments using one of the most
capable generalist web agent frameworks to date, SeeAct. The results
demonstrate that EIA achieves up to 70% ASR in stealing users' specific PII.
Stealing full user requests is more challenging, but a relaxed version of EIA
can still achieve 16% ASR. Despite these concerning results, it is important to
note that the attack can still be detectable through careful human inspection,
highlighting a trade-off between high autonomy and security. This leads to our
detailed discussion on the efficacy of EIA under different levels of human
supervision as well as implications on defenses for generalist web agents.Abstract
Rapid advances in the capabilities of large language models (LLMs) have
raised widespread concerns regarding their potential for malicious use.
Open-weight LLMs present unique challenges, as existing safeguards lack
robustness to tampering attacks that modify model weights. For example, recent
works have demonstrated that refusal and unlearning safeguards can be trivially
removed with a few steps of fine-tuning. These vulnerabilities necessitate new
approaches for enabling the safe release of open-weight LLMs. We develop a
method, called TAR, for building tamper-resistant safeguards into open-weight
LLMs such that adversaries cannot remove the safeguards even after thousands of
steps of fine-tuning. In extensive evaluations and red teaming analyses, we
find that our method greatly improves tamper-resistance while preserving benign
capabilities. Our results demonstrate that tamper-resistance is a tractable
problem, opening up a promising new avenue to improve the safety and security
of open-weight LLMs.Abstract
Advancing Depth Anything Model for Unsupervised Monocular Depth
Estimation in Endoscopy
Depth estimation is a cornerstone of 3D reconstruction and plays a vital role
in minimally invasive endoscopic surgeries. However, most current depth
estimation networks rely on traditional convolutional neural networks, which
are limited in their ability to capture global information. Foundation models
offer a promising avenue for enhancing depth estimation, but those currently
available are primarily trained on natural images, leading to suboptimal
performance when applied to endoscopic images. In this work, we introduce a
novel fine-tuning strategy for the Depth Anything Model and integrate it with
an intrinsic-based unsupervised monocular depth estimation framework. Our
approach includes a low-rank adaptation technique based on random vectors,
which improves the model's adaptability to different scales. Additionally, we
propose a residual block built on depthwise separable convolution to compensate
for the transformer's limited ability to capture high-frequency details, such
as edges and textures. Our experimental results on the SCARED dataset show that
our method achieves state-of-the-art performance while minimizing the number of
trainable parameters. Applying this method in minimally invasive endoscopic
surgery could significantly enhance both the precision and safety of these
procedures.Abstract
Revolutionizing Database Q&A with Large Language Models: Comprehensive
Benchmark and Evaluation
The development of Large Language Models (LLMs) has revolutionized Q&A across
various industries, including the database domain. However, there is still a
lack of a comprehensive benchmark to evaluate the capabilities of different
LLMs and their modular components in database Q&A. To this end, we introduce
DQA, the first comprehensive database Q&A benchmark. DQA features an innovative
LLM-based method for automating the generation, cleaning, and rewriting of
database Q&A, resulting in over 240,000 Q&A pairs in English and Chinese. These
Q&A pairs cover nearly all aspects of database knowledge, including database
manuals, database blogs, and database tools. This inclusion allows for
additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool
Invocation Generation (TIG) capabilities in the database Q&A task. Furthermore,
we propose a comprehensive LLM-based database Q&A testbed on DQA. This testbed
is highly modular and scalable, with both basic and advanced components like
Question Classification Routing (QCR), RAG, TIG, and Prompt Template
Engineering (PTE). Besides, DQA provides a complete evaluation pipeline,
featuring diverse metrics and a standardized evaluation process to ensure
comprehensiveness, accuracy, and fairness. We use DQA to evaluate the database
Q&A capabilities under the proposed testbed comprehensively. The evaluation
reveals findings like (i) the strengths and limitations of nine different
LLM-based Q&A bots and (ii) the performance impact and potential improvements
of various service components (e.g., QCR, RAG, TIG). We hope our benchmark and
findings will better guide the future development of LLM-based database Q&A
research.Abstract
D&M: Enriching E-commerce Videos with Sound Effects by Key Moment
Detection and SFX Matching
Videos showcasing specific products are increasingly important for
E-commerce. Key moments naturally exist as the first appearance of a specific
product, presentation of its distinctive features, the presence of a buying
link, etc. Adding proper sound effects (SFX) to these key moments, or video
decoration with SFX (VDSFX), is crucial for enhancing the user engaging
experience. Previous studies about adding SFX to videos perform video to SFX
matching at a holistic level, lacking the ability of adding SFX to a specific
moment. Meanwhile, previous studies on video highlight detection or video
moment retrieval consider only moment localization, leaving moment to SFX
matching untouched. By contrast, we propose in this paper D&M, a unified method
that accomplishes key moment detection and moment to SFX matching
simultaneously. Moreover, for the new VDSFX task we build a large-scale dataset
SFX-Moment from an E-commerce platform. For a fair comparison, we build
competitive baselines by extending a number of current video moment detection
methods to the new task. Extensive experiments on SFX-Moment show the superior
performance of the proposed method over the baselines. Code and data will be
released.Abstract
Event Stream based Sign Language Translation: A High-Definition
Benchmark Dataset and A New Algorithm
First Large-scale and High-Definition Benchmark Dataset for
Event-based Sign Language Translation
Sign Language Translation (SLT) is a core task in the field of AI-assisted
disability. Unlike traditional SLT based on visible light videos, which is
easily affected by factors such as lighting, rapid hand movements, and privacy
breaches, this paper proposes the use of high-definition Event streams for SLT,
effectively mitigating the aforementioned issues. This is primarily because
Event streams have a high dynamic range and dense temporal signals, which can
withstand low illumination and motion blur well. Additionally, due to their
sparsity in space, they effectively protect the privacy of the target person.
More specifically, we propose a new high-resolution Event stream sign language
dataset, termed Event-CSL, which effectively fills the data gap in this area of
research. It contains 14,827 videos, 14,821 glosses, and 2,544 Chinese words in
the text vocabulary. These samples are collected in a variety of indoor and
outdoor scenes, encompassing multiple angles, light intensities, and camera
movements. We have benchmarked existing mainstream SLT works to enable fair
comparison for future efforts. Based on this dataset and several other
large-scale datasets, we propose a novel baseline method that fully leverages
the Mamba model's ability to integrate temporal information of CNN features,
resulting in improved sign language translation outcomes. Both the benchmark
dataset and source code will be released on
https://github.com/Event-AHU/OpenESLAbstract
Molecular optimization is a crucial aspect of drug discovery, aimed at
refining molecular structures to enhance drug efficacy and minimize side
effects, ultimately accelerating the overall drug development process. Many
target-based molecular optimization methods have been proposed, significantly
advancing drug discovery. These methods primarily on understanding the specific
drug target structures or their hypothesized roles in combating diseases.
However, challenges such as a limited number of available targets and a
difficulty capturing clear structures hinder innovative drug development. In
contrast, phenotypic drug discovery (PDD) does not depend on clear target
structures and can identify hits with novel and unbiased polypharmacology
signatures. As a result, PDD-based molecular optimization can reduce potential
safety risks while optimizing phenotypic activity, thereby increasing the
likelihood of clinical success. Therefore, we propose a fragment-masked
molecular optimization method based on PDD (FMOP). FMOP employs a
regression-free diffusion model to conditionally optimize the molecular masked
regions without training, effectively generating new molecules with similar
scaffolds. On the large-scale drug response dataset GDSCv2, we optimize the
potential molecules across all 945 cell lines. The overall experiments
demonstrate that the in-silico optimization success rate reaches 94.4%, with an
average efficacy increase of 5.3%. Additionally, we conduct extensive ablation
and visualization experiments, confirming that FMOP is an effective and robust
molecular optimization method. The code is available
at:https://anonymous.4open.science/r/FMOP-98C2.Abstract
arXiv:2407.21783v2 »Full PDF »Modern artificial intelligence (AI) systems are powered by foundation models.
This paper presents a new set of foundation models, called Llama 3. It is a
herd of language models that natively support multilinguality, coding,
reasoning, and tool usage. Our largest model is a dense Transformer with 405B
parameters and a context window of up to 128K tokens. This paper presents an
extensive empirical evaluation of Llama 3. We find that Llama 3 delivers
comparable quality to leading language models such as GPT-4 on a plethora of
tasks. We publicly release Llama 3, including pre-trained and post-trained
versions of the 405B parameter language model and our Llama Guard 3 model for
input and output safety. The paper also presents the results of experiments in
which we integrate image, video, and speech capabilities into Llama 3 via a
compositional approach. We observe this approach performs competitively with
the state-of-the-art on image, video, and speech recognition tasks. The
resulting models are not yet being broadly released as they are still under
development.Abstract
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