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Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
Mar 06, 2024
We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we show that it is possible to conceptualize the creation of execution triggers as a differentiable search problem and use learning-based methods to autonomously generate them. Our results demonstrate that a motivated adversary can forge triggers that are not only drastically more effective than current handcrafted ones but also exhibit inherent flexibility in shape, properties, and functionality. In this direction, we show that an attacker can design and generate Neural Execs capable of persisting through multi-stage preprocessing pipelines, such as in the case of Retrieval-Augmented Generation (RAG)-based applications. More critically, our findings show that attackers can produce triggers that deviate markedly in form and shape from any known attack, sidestepping existing blacklist-based detection and sanitation approaches.
Can Decentralized Learning be more robust than Federated Learning?
Mar 07, 2023
Decentralized Learning (DL) is a peer--to--peer learning approach that allows a group of users to jointly train a machine learning model. To ensure correctness, DL should be robust, i.e., Byzantine users must not be able to tamper with the result of the collaboration. In this paper, we introduce two \textit{new} attacks against DL where a Byzantine user can: make the network converge to an arbitrary model of their choice, and exclude an arbitrary user from the learning process. We demonstrate our attacks' efficiency against Self--Centered Clipping, the state--of--the--art robust DL protocol. Finally, we show that the capabilities decentralization grants to Byzantine users result in decentralized learning \emph{always} providing less robustness than federated learning.
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
Jan 18, 2023
We develop the first universal password model -- a password model that, once pre-trained, can automatically adapt to any password distribution. To achieve this result, the model does not need to access any plaintext passwords from the target set. Instead, it exploits users' auxiliary information, such as email addresses, as a proxy signal to predict the underlying target password distribution. The model uses deep learning to capture the correlation between the auxiliary data of a group of users (e.g., users of a web application) and their passwords. It then exploits those patterns to create a tailored password model for the target community at inference time. No further training steps, targeted data collection, or prior knowledge of the community's password distribution is required. Besides defining a new state-of-the-art for password strength estimation, our model enables any end-user (e.g., system administrators) to autonomously generate tailored password models for their systems without the often unworkable requirement of collecting suitable training data and fitting the underlying password model. Ultimately, our framework enables the democratization of well-calibrated password models to the community, addressing a major challenge in the deployment of password security solutions on a large scale.
On the Privacy of Decentralized Machine Learning
May 17, 2022
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at circumventing the main limitations of federated learning. We identify the decentralized learning properties that affect users' privacy and we introduce a suite of novel attacks for both passive and active decentralized adversaries. We demonstrate that, contrary to what is claimed by decentralized learning proposers, decentralized learning does not offer any security advantages over more practical approaches such as federated learning. Rather, it tends to degrade users' privacy by increasing the attack surface and enabling any user in the system to perform powerful privacy attacks such as gradient inversion, and even gain full control over honest users' local model. We also reveal that, given the state of the art in protections, privacy-preserving configurations of decentralized learning require abandoning any possible advantage over the federated setup, completely defeating the objective of the decentralized approach.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency
Nov 14, 2021
Federated learning allows a set of users to train a deep neural network over their private training datasets. During the protocol, datasets never leave the devices of the respective users. This is achieved by requiring each user to send "only" model updates to a central server that, in turn, aggregates them to update the parameters of the deep neural network. However, it has been shown that each model update carries sensitive information about the user's dataset (e.g., gradient inversion attacks). The state-of-the-art implementations of federated learning protect these model updates by leveraging secure aggregation: A cryptographic protocol that securely computes the aggregation of the model updates of the users. Secure aggregation is pivotal to protect users' privacy since it hinders the server from learning the value and the source of the individual model updates provided by the users, preventing inference and data attribution attacks. In this work, we show that a malicious server can easily elude secure aggregation as if the latter were not in place. We devise two different attacks capable of inferring information on individual private training datasets, independently of the number of users participating in the secure aggregation. This makes them concrete threats in large-scale, real-world federated learning applications. The attacks are generic and do not target any specific secure aggregation protocol. They are equally effective even if the secure aggregation protocol is replaced by its ideal functionality that provides the perfect level of security. Our work demonstrates that secure aggregation has been incorrectly combined with federated learning and that current implementations offer only a "false sense of security".
Unleashing the Tiger: Inference Attacks on Split Learning
Dec 04, 2020
We investigate the security of split learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the paper, we make explicit the vulnerabilities of the protocol and demonstrate its inherent insecurity by introducing general attack strategies targeting the reconstruction of clients' private training sets. More prominently, we demonstrate that a malicious server can actively hijack the learning process of the distributed model and bring it into an insecure state that enables inference attacks on clients' data. We implement different adaptations of the attack and test them on various datasets as well as within realistic threat scenarios. To make our results reproducible, we made our code available at https://github.com/pasquini-dario/SplitNN_FSHA.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
Oct 26, 2020
Password security hinges on an accurate understanding of the techniques adopted by attackers. However, current studies mostly rely on probabilistic password models that are imperfect proxies of real-world guessing strategies. The main reason is that attackers rely on very pragmatic approaches such as dictionary attacks. Unfortunately, it is inherently difficult to correctly model those methods. To be representative, dictionary attacks must be thoughtfully configured according to a process that requires an expertise that cannot be easily replicated in password studies. The consequence of inaccurately calibrating those attacks is the unreliability of password security estimates, impaired by measurement bias. In the present work, we introduce new guessing techniques that make dictionary attacks consistently more resilient to inadequate configurations. Our framework allows dictionary attacks to self-heal and converge towards optimal attacks' performance, requiring no supervision or domain-knowledge. To achieve this: (1) We use a deep neural network to model and then simulate the proficiency of expert adversaries. (2) Then, we introduce automatic dynamic strategies within dictionary attacks to mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets. Our techniques enable robust and sound password strength estimates, eventually reducing bias in modeling real-world threats in password security.
Interpretable Probabilistic Password Strength Meters via Deep Learning
Apr 29, 2020
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.
Out-domain examples for generative models
Mar 07, 2019
Deep generative models are being increasingly used in a wide variety of applications. However, the generative process is not fully predictable and at times, it produces an unexpected output. We will refer to those outputs as out-domain examples. In the present paper we show that an attacker can force a pre-trained generator to reproduce an arbitrary out-domain example if fed by a suitable adversarial input. The main assumption is that those outputs lie in an unexplored region of the generator's codomain and hence they have a very low probability of being naturally generated. Moreover, we show that this adversarial input can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The goal is to look for an efficient way of finding these inputs in the generator's latent space.