Backdoor Attack on Multilingual Machine Translation

While multilingual machine translation (MNMT) systems hold substantial promise, they also have security vulnerabilities. Our research highlights that MNMT systems can be susceptible to a particularly devious style of backdoor attack, whereby an attacker injects poisoned data into a low-resource language pair to cause malicious translations in other languages, including high-resource languages. Our experimental results reveal that injecting less than 0.01% poisoned data into a low-resource language pair can achieve an average 20% attack success rate in attacking high-resource language pairs. This type of attack is of particular concern, given the larger attack surface of languages inherent to low-resource settings. Our aim is to bring attention to these vulnerabilities within MNMT systems with the hope of encouraging the community to address security concerns in machine translation, especially in the context of low-resource languages.

It's Simplex! Disaggregating Measures to Improve Certified Robustness

Certified robustness circumvents the fragility of defences against adversarial attacks, by endowing model predictions with guarantees of class invariance for attacks up to a calculated size. While there is value in these certifications, the techniques through which we assess their performance do not present a proper accounting of their strengths and weaknesses, as their analysis has eschewed consideration of performance over individual samples in favour of aggregated measures. By considering the potential output space of certified models, this work presents two distinct approaches to improve the analysis of certification mechanisms, that allow for both dataset-independent and dataset-dependent measures of certification performance. Embracing such a perspective uncovers new certification approaches, which have the potential to more than double the achievable radius of certification, relative to current state-of-the-art. Empirical evaluation verifies that our new approach can certify $9\%$ more samples at noise scale $\sigma = 1$, with greater relative improvements observed as the difficulty of the predictive task increases.

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks

Poisoning attacks can disproportionately influence model behaviour by making small changes to the training corpus. While defences against specific poisoning attacks do exist, they in general do not provide any guarantees, leaving them potentially countered by novel attacks. In contrast, by examining worst-case behaviours Certified Defences make it possible to provide guarantees of the robustness of a sample against adversarial attacks modifying a finite number of training samples, known as pointwise certification. We achieve this by exploiting both Differential Privacy and the Sampled Gaussian Mechanism to ensure the invariance of prediction for each testing instance against finite numbers of poisoned examples. In doing so, our model provides guarantees of adversarial robustness that are more than twice as large as those provided by prior certifications.

Exploiting Certified Defences to Attack Randomised Smoothing

In guaranteeing that no adversarial examples exist within a bounded region, certification mechanisms play an important role in neural network robustness. Concerningly, this work demonstrates that the certification mechanisms themselves introduce a new, heretofore undiscovered attack surface, that can be exploited by attackers to construct smaller adversarial perturbations. While these attacks exist outside the certification region in no way invalidate certifications, minimising a perturbation's norm significantly increases the level of difficulty associated with attack detection. In comparison to baseline attacks, our new framework yields smaller perturbations more than twice as frequently as any other approach, resulting in an up to $34 \%$ reduction in the median perturbation norm. That this approach also requires $90 \%$ less computational time than approaches like PGD. That these reductions are possible suggests that exploiting this new attack vector would allow attackers to more frequently construct hard to detect adversarial attacks, by exploiting the very systems designed to defend deployed models.

Certified Robustness of Learning-based Static Malware Detectors

Certified defenses are a recent development in adversarial machine learning (ML), which aim to rigorously guarantee the robustness of ML models to adversarial perturbations. A large body of work studies certified defenses in computer vision, where $\ell_p$ norm-bounded evasion attacks are adopted as a tractable threat model. However, this threat model has known limitations in vision, and is not applicable to other domains -- e.g., where inputs may be discrete or subject to complex constraints. Motivated by this gap, we study certified defenses for malware detection, a domain where attacks against ML-based systems are a real and current threat. We consider static malware detection systems that operate on byte-level data. Our certified defense is based on the approach of randomized smoothing which we adapt by: (1) replacing the standard Gaussian randomization scheme with a novel deletion randomization scheme that operates on bytes or chunks of an executable; and (2) deriving a certificate that measures robustness to evasion attacks in terms of generalized edit distance. To assess the size of robustness certificates that are achievable while maintaining high accuracy, we conduct experiments on malware datasets using a popular convolutional malware detection model, MalConv. We are able to accurately classify 91% of the inputs while being certifiably robust to any adversarial perturbations of edit distance 128 bytes or less. By comparison, an existing certification of up to 128 bytes of substitutions (without insertions or deletions) achieves an accuracy of 78%. In addition, given that robustness certificates are conservative, we evaluate practical robustness to several recently published evasion attacks and, in some cases, find robustness beyond certified guarantees.

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity

In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of predictions to all norm-bounded attacks is achieved through randomised smoothing of network inputs. Today's state-of-the-art certifications make optimal use of the class output scores at the input instance under test: no better radius of certification (under the $L_2$ norm) is possible given only these score. However, it is an open question as to whether such lower bounds can be improved using local information around the instance under test. In this work, we demonstrate how today's "optimal" certificates can be improved by exploiting both the transitivity of certifications, and the geometry of the input space, giving rise to what we term Geometrically-Informed Certified Robustness. By considering the smallest distance to points on the boundary of a set of certifications this approach improves certifications for more than $80\%$ of Tiny-Imagenet instances, yielding an on average $5 \%$ increase in the associated certification. When incorporating training time processes that enhance the certified radius, our technique shows even more promising results, with a uniform $4$ percentage point increase in the achieved certified radius.

Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes

The sample compressibility of concept classes plays an important role in learning theory, as a sufficient condition for PAC learnability, and more recently as an avenue for robust generalisation in adaptive data analysis. Whether compression schemes of size $O(d)$ must necessarily exist for all classes of VC dimension $d$ is unknown, but conjectured to be true by Warmuth. Recently Chalopin, Chepoi, Moran, and Warmuth (2018) gave a beautiful unlabelled sample compression scheme of size VC dimension for all maximum classes: classes that meet the Sauer-Shelah-Perles Lemma with equality. They also offered a counterexample to compression schemes based on a promising approach known as corner peeling. In this paper we simplify and extend their proof technique to deal with so-called extremal classes of VC dimension $d$ which contain maximum classes of VC dimension $d-1$. A criterion is given which would imply that all extremal classes admit unlabelled compression schemes of size $d$. We also prove that all intersection-closed classes with VC dimension $d$ admit unlabelled compression schemes of size at most $11d$.

Testing the Robustness of Learned Index Structures

While early empirical evidence has supported the case for learned index structures as having favourable average-case performance, little is known about their worst-case performance. By contrast, classical structures are known to achieve optimal worst-case behaviour. This work evaluates the robustness of learned index structures in the presence of adversarial workloads. To simulate adversarial workloads, we carry out a data poisoning attack on linear regression models that manipulates the cumulative distribution function (CDF) on which the learned index model is trained. The attack deteriorates the fit of the underlying ML model by injecting a set of poisoning keys into the training dataset, which leads to an increase in the prediction error of the model and thus deteriorates the overall performance of the learned index structure. We assess the performance of various regression methods and the learned index implementations ALEX and PGM-Index. We show that learned index structures can suffer from a significant performance deterioration of up to 20% when evaluated on poisoned vs. non-poisoned datasets.

State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing

The statefulness property of network protocol implementations poses a unique challenge for testing and verification techniques, including Fuzzing. Stateful fuzzers tackle this challenge by leveraging state models to partition the state space and assist the test generation process. Since not all states are equally important and fuzzing campaigns have time limits, fuzzers need effective state selection algorithms to prioritize progressive states over others. Several state selection algorithms have been proposed but they were implemented and evaluated separately on different platforms, making it hard to achieve conclusive findings. In this work, we evaluate an extensive set of state selection algorithms on the same fuzzing platform that is AFLNet, a state-of-the-art fuzzer for network servers. The algorithm set includes existing ones supported by AFLNet and our novel and principled algorithm called AFLNetLegion. The experimental results on the ProFuzzBench benchmark show that (i) the existing state selection algorithms of AFLNet achieve very similar code coverage, (ii) AFLNetLegion clearly outperforms these algorithms in selected case studies, but (iii) the overall improvement appears insignificant. These are unexpected yet interesting findings. We identify problems and share insights that could open opportunities for future research on this topic.