TrojanPuzzle: Covertly Poisoning Code-Suggestion Models

With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources. As a result, these models are susceptible to data poisoning attacks where an adversary manipulates the model's training or fine-tuning phases by injecting malicious data. Poisoning attacks could be designed to influence the model's suggestions at run time for chosen contexts, such as inducing the model into suggesting insecure code payloads. To achieve this, prior poisoning attacks explicitly inject the insecure code payload into the training data, making the poisoning data detectable by static analysis tools that can remove such malicious data from the training set. In this work, we demonstrate two novel data poisoning attacks, COVERT and TROJANPUZZLE, that can bypass static analysis by planting malicious poisoning data in out-of-context regions such as docstrings. Our most novel attack, TROJANPUZZLE, goes one step further in generating less suspicious poisoning data by never including certain (suspicious) parts of the payload in the poisoned data, while still inducing a model that suggests the entire payload when completing code (i.e., outside docstrings). This makes TROJANPUZZLE robust against signature-based dataset-cleansing methods that identify and filter out suspicious sequences from the training data. Our evaluation against two model sizes demonstrates that both COVERT and TROJANPUZZLE have significant implications for how practitioners should select code used to train or tune code-suggestion models.

VENOMAVE: Clean-Label Poisoning Against Speech Recognition

In the past few years, we observed a wide adoption of practical systems that use Automatic Speech Recognition (ASR) systems to improve human-machine interaction. Modern ASR systems are based on neural networks and prior research demonstrated that these systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's network during the system's run time. The research question if ASR systems are also vulnerable to data poisoning attacks is still unanswered. In such an attack, a manipulation happens during the training phase of the neural network: an adversary injects malicious inputs into the training set such that the neural network's integrity and performance are compromised. In this paper, we present the first data poisoning attack in the audio domain, called VENOMAVE. Prior work in the image domain demonstrated several types of data poisoning attacks, but they cannot be applied to the audio domain. The main challenge is that we need to attack a time series of inputs. To enforce a targeted misclassification in an ASR system, we need to carefully generate a specific sequence of disturbed inputs for the target utterance, which will eventually be decoded to the desired sequence of words. More specifically, the adversarial goal is to produce a series of misclassification tasks and in each of them, we need to poison the system to misrecognize each frame of the target file. To demonstrate the practical feasibility of our attack, we evaluate VENOMAVE on an ASR system that detects sequences of digits from 0 to 9. When poisoning only 0.94% of the dataset on average, we achieve an attack success rate of 83.33%. We conclude that data poisoning attacks against ASR systems represent a real threat that needs to be considered.

Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poisoned samples are injected in the training dataset. While these poisons look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label attack, Bullseye Polytope, which creates poison images centered around the target image in the feature space. Bullseye Polytope improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end training, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) in crafting the poisoned samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without increasing the number of poisons.

Detecting Deceptive Reviews using Generative Adversarial Networks

In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detecting deceptive reviews. Unlike standard GAN models which have a single Generator and Discriminator model, FakeGAN uses two discriminator models and one generative model. The generator is modeled as a stochastic policy agent in reinforcement learning (RL), and the discriminators use Monte Carlo search algorithm to estimate and pass the intermediate action-value as the RL reward to the generator. Providing the generator model with two discriminator models avoids the mod collapse issue by learning from both distributions of truthful and deceptive reviews. Indeed, our experiments show that using two discriminators provides FakeGAN high stability, which is a known issue for GAN architectures. While FakeGAN is built upon a semi-supervised classifier, known for less accuracy, our evaluation results on a dataset of TripAdvisor hotel reviews show the same performance in terms of accuracy as of the state-of-the-art approaches that apply supervised machine learning. These results indicate that GANs can be effective for text classification tasks. Specifically, FakeGAN is effective at detecting deceptive reviews.