• A discrete prompt optimization pipeline leveraging genetic algorithm, equipped with security-focused scoring and mutation functions for secure code generation. • Introduces two new security-specific LLM-assisted prompt mutation techniques called self-guided and feedback-guided mutation to generate more security oriented variations of code generation prompts. • The security-specific mutation techniques led to prompts with richer security cues compared to the prompts mutated by just using the generic techniques such as paraphrase, back translation and cloze transformation. • Prompts optimized using a combination of security-specific and generic mutation techniques led to the most improvement in the security of the LLM-generated code. • Prompts optimized on one LLM demonstrated lack of transferability to others indicating the importance of model-specific optimizations. Large language models (LLMs) have become powerful tools that enable novice developers to generate production-level code. However, research has highlighted the security risks associated with such code generation, due to the high volume of generated software vulnerabilities. Recent studies have explored various techniques for automatically optimizing prompts to elicit desired responses from LLMs. Among these methods, Genetic Algorithms (GAs), which search for optimal solutions by evolving an initial population of candidates through iterative mutations, have gained attention as a lightweight and effective prompt optimization approach that does not require large datasets or access to model weights. However, their potential has not yet been examined in the context of secure code generation. In this paper, we use GA to develop a discrete prompt optimization pipeline specifically designed for secure code generation. We introduce two domain-specific prompt mutation techniques and assess how incorporating these security-focused mutations alongside general-purpose techniques, such as back translation and paraphrasing, affects the security of Python code generated by LLMs. Results demonstrate that our security-specific mutation techniques led to prompts with richer security context compared to the generic mutation techniques. Furthermore, combining these techniques with generic mutations substantially reduced the number of security weaknesses in the LLM-generated code. We also observed that prompts optimized for a particular LLM tend to perform best on that same model, highlighting the importance of model-specific prompt optimization.

Abstract To address the extremely concerning problem of software vulnerability, system security is often entrusted to Machine Learning (ML) algorithms. Despite their now established detection capabilities, such models are limited by design to flagging the entire input source code function as vulnerable, rather than precisely localizing the concerned code lines. However, the detection granularity is crucial to support human operators during software development, ensuring that such predictions reflect the true code semantics to help debug, evaluate, and fix the detected vulnerabilities. To address this issue, recent work made progress toward improving the detector’s localization ability, thus narrowing down the vulnerability detection “window” and providing more fine-grained predictions. Such approaches, however, implicitly disregard the presence of spurious correlations and biases in the data, which often predominantly influence the performance of ML algorithms. In this work, we investigate how detectors comply with this requirement by proposing an explainability-based evaluation procedure. Our approach, defined as Detection Alignment (DA), quantifies the agreement between the input source code lines that most influence the prediction and the actual localization of the vulnerability as per the ground truth. Through DA, which is model-agnostic and adaptable to different detection tasks, not limited to our use case, we analyze multiple learning-based vulnerability detectors and datasets. As a result, we show how the predictions of such models are consistently biased by non-vulnerable lines, ultimately highlighting the high impact of biases and spurious correlations.

Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must learn new classes over time without forgetting previous ones and handle inputs from unknown classes that a closed-set model would misclassify. In this paper, we present an in-depth analysis of post-hoc OOD detection methods and investigate their potential to eliminate the need for a memory buffer. When post hoc OOD detection is applied at inference time, we discover that it can effectively replace buffer-based strategies. We examine the performance of these methods in terms of classification accuracy of seen samples and rejection rates of unseen samples. We show that our approach achieves competitive performance compared to recent multi-head and single-head methods that rely on memory buffers and other buffer-free approaches. The results show that the proposed approach outperforms them in a closed-world setting and detects unseen samples while being significantly resource-efficient. Experimental results on CIFAR-10, CIFAR-100, and Tiny ImageNet support our findings and offer new insights into the design of efficient and privacy-preserving CIL systems for open-world settings.

Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning (CL) has emerged as a scalable alternative, enabling incremental updates without full data access while mitigating catastrophic forgetting. In this work, we analyze a critical yet overlooked issue in this context: security regression. Unlike forgetting, which manifests as a drop in average performance on previously seen data, security regression captures harmful sample-level prediction changes, e.g., malware samples that were correctly detected before an update but evade detection afterward. This poses serious risks in security-critical applications, as the silent reintroduction of previously detected threats may undermine users' trust in the update process, leading them to perceive a regression in security even if the average model performance has actually improved. We first formalize and quantify security regression in CL-based malware detectors, revealing that up to 3-6% of malware experience it after model updates. We then address this issue by introducing a regression-aware framework to the CL setting. Specifically, we instantiate it via Positive Congruent Training (PCT), showing seamless integration with any prior CL strategy. Experiments on the ELSA, Tesseract, and AZ-Class datasets show that our method effectively halves regression across different CL scenarios while maintaining strong detection performance over time.

While novel gradient-based attacks are continuously proposed to improve the optimization of adversarial examples, each is shown to outperform its predecessors using different experimental setups, implementations, and computational budgets, leading to biased and unfair comparisons. In this work, we overcome this issue by proposing AttackBench, i.e., an attack evaluation framework that evaluates the effectiveness of each attack (along with its different library implementations) under the same maximum available computational budget. To this end, we (i) define a novel optimality metric that quantifies how close each attack is to the optimal solution (empirically estimated by ensembling all attacks), and (ii) limit the maximum number of forward and backward queries that each attack can execute on the target model. Our extensive experimental analysis compares more than 100 attack implementations over 800 different configurations, considering both CIFAR-10 and ImageNet models, and shows that only a few attack implementations outperform all the remaining approaches. These findings suggest that novel defenses should be evaluated against different attacks than those normally used in the literature to avoid overly-optimistic robustness evaluations. We release AttackBench as a publicly-available benchmark that will be continuously updated with new attack implementations to maintain an up-to-date ranking of the best gradient-based attacks. We release AttackBench as a publicly available benchmark, including a continuously updated leaderboard and source code to maintain an up-to-date ranking of the best gradient-based attacks.

The use of Artificial Intelligence (AI) and Machine Learning (ML) has been the center of the most outstanding advancements in the last years. The ability to analyze considerable streams of data in real time makes these technologies the most promising tool in many domains, including cybersecurity. As an outstanding example, ML can be used for identifying malware because of its ability to detect patterns otherwise difficult to see for humans and hardcoded rules. However, the use of AI and ML in security-relevant domains raised rightful concerns about their trustworthiness and robustness, especially when facing adaptive attackers. Additionally, privacy threats are now emerging as a crucial aspect and need proper testing and possibly mitigation to prevent data stealing and leakage of sensitive information. AISec provides a venue for presenting and discussing new developments in the intersection of security and privacy with AI and ML.

Ensemble-based black-box transfer attacks optimize adversarial examples on a set of surrogate models, claiming to reach high success rates by querying the (unknown) target model only a few times. In this work, we show that prior evaluations are systematically biased, as such methods are tested only under overly optimistic scenarios, without considering (i) how the choice of surrogate models influences transferability, (ii) how they perform against robust target models, and (iii) whether querying the target to refine the attack is really required.To address these gaps, we introduce TransferBench, a framework for evaluating ensemble-based black-box transfer attacks under more realistic and challenging scenarios than prior work. Our framework considers 17 distinct settings on CIFAR-10 and ImageNet, including diverse surrogate-target combinations, robust targets, and comparisons to baseline methods that do not use any query-based refinement mechanism. Our findings reveal that existing methods fail to generalize to more challenging scenarios, and that query-based refinement offers little to no benefit, contradicting prior claims. These results highlight that building reliable and query-efficient black-box transfer attacks remains an open challenge. We release our benchmark and evaluation code at: https://github.com/pralab/transfer-bench.

Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size schedulers, and default hyperparameters. In this work, we tackle these limitations by proposing a parametric variation of the well-known fast minimum-norm attack algorithm, whose loss, optimizer, step-size scheduler, and hyperparameters can be dynamically adjusted. We re-evaluate 12 robust models, showing that our attack finds smaller adversarial perturbations without requiring any additional tuning. This also enables reporting adversarial robustness as a function of the perturbation budget, providing a more complete evaluation than that offered by fixed-budget attacks, while remaining efficient. We release our open-source code at https://github.com/pralab/HO-FMN . • We improve minimum-norm attacks using different losses, optimizers, and schedulers. • We leverage hyperparameter optimization to improve the attack performance. • We compute full robustness-perturbation curves with a single, effective attack.

Machine-learning models demand periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly updated model may commit mistakes the previous model did not make. Such misclassifications are referred to as negative flips, experienced by users as a regression of performance. In this work, we show that this problem also affects robustness to adversarial examples, hindering the development of secure model update practices. In particular, when updating a model to improve its adversarial robustness, previously ineffective adversarial attacks on some inputs may become successful, causing a regression in the perceived security of the system. We propose a novel technique, named robustness-congruent adversarial training, to address this issue. It amounts to fine-tuning a model with adversarial training, while constraining it to retain higher robustness on the samples for which no adversarial example was found before the update. We show that our algorithm and, more generally, learning with non-regression constraints, provides a theoretically-grounded framework to train consistent estimators. Our experiments on robust models for computer vision confirm that both accuracy and robustness, even if improved after model update, can be affected by negative flips, and our robustness-congruent adversarial training can mitigate the problem, outperforming competing baseline methods.

Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs inducing a misclassification. These methods, which we refer to as adversarial pruning methods, involve complex and articulated designs, making it difficult to analyze the differences and establish a fair and accurate comparison. In this work, we overcome these issues by surveying current adversarial pruning methods and proposing a novel robustness-oriented taxonomy to categorize them based on two main dimensions: the pipeline , defining when to prune; and the specifics , defining how to prune. We then highlight the limitations of current empirical analyses and propose a novel, fair evaluation benchmark to address them. We finally conduct an empirical re-evaluation of current adversarial pruning methods and discuss the results, highlighting the shared traits of top-performing adversarial pruning methods, as well as common issues. We welcome contributions in our publicly-available benchmark at https://github.com/pralab/AdversarialPruningBenchmark .

The use of Artificial Intelligence (AI) and Machine Learning (ML) has been the center of the most outstanding advancements in the last years. The ability to analyze considerable streams of data in real time makes these technologies the most promising tool in many domains, including cybersecurity. As an outstanding example, ML can be used for identifying malware because of its ability to detect patterns otherwise difficult to see for humans and hard-coded rules. However, the use of AI and ML in security-relevant domains raised rightful concerns about their trustworthiness and robustness, espe- cially in front of adaptive attackers. Additionally, privacy threats are now emerging as a crucial aspect and need proper testing and possibly mitigation to prevent data stealing and leakage of sensitive information. AISec provides a venue for presenting and discussing new developments in the intersection of security and privacy with AI and ML. The AISec'24 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3658644.3691545.

The use of Artificial Intelligence (AI) and Machine Learning (ML) has been the center of the most outstanding advancements in the last years. The ability to analyze considerable streams of data in real time makes these technologies the most promising tool in many domains, including cybersecurity. As an outstanding example, ML can be used for identifying malware because of its ability to detect patterns otherwise difficult to see for humans and hard-coded rules. As malware continues to evolve, ML will become increasingly important for keeping up with the latest threats. However, the use of AI and ML in security-relevant domains raised rightful concerns about their trustworthiness and robustness, especially in front of adaptive attackers. Additionally, privacy threats are now emerging as a crucial aspect and need proper testing and possibly mitigation to prevent data stealing and leakage of sensitive information. The AISec workshop provides a venue for presenting and discussing new developments in the intersection of security and privacy with AI and ML. The complete AISec'23 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3576915.3624029.

In recent years, machine learning has become the most effective way to analyze massive data streams.However, machine learning is also subject to security and reliability issues.These aspects require machine learning to be thoroughly tested before being deployed in unsupervised scenarios, such as services intended for consumers.The goal of this session is to discuss open challenges, both theoretical and practical, related to the security and safety of machine learning.The session will try to address the following challenges: (i) the implementation of efficient tests for Machine Learning in the context of robustness to attacks and natural drifts of data; and (ii) the design of robust and efficient models able to function in the wild and mitigate or detect adversarial attacks.

Evaluating the adversarial robustness of machine-learning models using gradient-based attacks is challenging.In this work, we show that hyperparameter optimization can improve fast minimum-norm attacks by automating the selection of the loss function, the optimizer, and the step-size scheduler, along with the corresponding hyperparameters.Our extensive evaluation involving several robust models demonstrates the improved efficacy of fast minimum-norm attacks when hyped up with hyperparameter optimization.We release our open-source code at https://github.com/pralab/HO-FMN.

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries.

Adversarial reprogramming allows repurposing a machine-learning model to perform a different task. For example, a model trained to recognize animals can be reprogrammed to recognize digits by embedding an adversarial program in the digit images provided as input. Recent work has shown that adversarial reprogramming may not only be used to abuse machine-learning models provided as a service, but also beneficially, to improve transfer learning when training data is scarce. However, the factors affecting its success are still largely unexplained. In this work, we develop a first-order linear model of adversarial reprogramming to show that its success inherently depends on the size of the average input gradient, which grows when input gradients are more aligned, and when inputs have higher dimensionality. The results of our experimental analysis, involving fourteen distinct reprogramming tasks, show that the above factors are correlated with the success and the failure of adversarial reprogramming.

Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model trained to recognize images of animals can be reprogrammed to recognize medical images by embedding an adversarial program in the images provided as inputs. This attack can be perpetrated even if the target model is a black box, supposed that the machine-learning model is provided as a service and the attacker can query the model and collect its outputs. So far, no defense has been demonstrated effective in this scenario. We show for the first time that this attack is detectable using stateful defenses, which store the queries made to the classifier and detect the abnormal cases in which they are similar. Once a malicious query is detected, the account of the user who made it can be blocked. Thus, the attacker must create many accounts to perpetrate the attack. To decrease this number, the attacker could create the adversarial program against a surrogate classifier and then fine-tune it by making a few queries to the target model. In this scenario, the effectiveness of the stateful defense is reduced, but we show that it is still effective.

Machine learning has been successfully used for increasingly complex and critical tasks, achieving high performance and efficiency that would not be possible for human operators. Unfortunately, recent studies have shown that, despite its power, this technology tends to learn spurious correlations from data, making it weak and susceptible to manipulation. Explainability techniques are often used to identify the most relevant features contributing to the decision. However, this is often done by taking examples one by one and trying to show the problem locally. To mitigate this issue, we propose in this paper a systematic method to leverage explainability techniques and build on their results to highlight problems in the model design and training. With an empirical analysis on the Devign dataset, we validate the proposed methodology with a CodeBERT model trained for vulnerability discovery, showing that, despite its impressive performances, spurious correlations consistently steer its decision.

Evaluating robustness of machine-learning models to adversarial examples is a challenging problem. Many defenses have been shown to provide a false sense of robustness by causing gradient-based attacks to fail, and they have been broken under more rigorous evaluations.Although guidelines and best practices have been suggested to improve current adversarial robustness evaluations, the lack of automatic testing and debugging tools makes it difficult to apply these recommendations in a systematic manner.In this work, we overcome these limitations by: (i) categorizing attack failures based on how they affect the optimization of gradient-based attacks, while also unveiling two novel failures affecting many popular attack implementations and past evaluations; (ii) proposing six novel \emph{indicators of failure}, to automatically detect the presence of such failures in the attack optimization process; and (iii) suggesting a systematic protocol to apply the corresponding fixes. Our extensive experimental analysis, involving more than 15 models in 3 distinct application domains, shows that our indicators of failure can be used to debug and improve current adversarial robustness evaluations, thereby providing a first concrete step towards automatizing and systematizing them. Our open-source code is available at: https://github.com/pralab/IndicatorsOfAttackFailure.

Evaluating adversarial robustness is a challenging problem.Many defenses have been shown to provide a false sense of security by unintentionally obfuscating gradients, hindering the optimization process of gradient-based attacks.Such defenses have been subsequently shown to fail against adaptive attacks crafted to circumvent gradient obfuscation.In this work, we present Slope, a metric that detects obfuscated gradients by comparing the expected and the actual increase of the attack loss after one iteration.We show that our metric can detect the presence of obfuscated gradients in many documented cases, providing a useful debugging tool towards improving adversarial robustness evaluations.

Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks to be carefully tuned, initialized, and possibly executed for many computationally-demanding iterations, even if specialized to a given perturbation model.In this work, we overcome these limitations by proposing a fast minimum-norm (FMN) attack that works with different $\ell_p$-norm perturbation models ($p=0, 1, 2, \infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\ell_p$-norm constraint of size $\epsilon$, while adapting $\epsilon$ to minimize the distance of the current sample to the decision boundary.Extensive experiments show that FMN significantly outperforms existing $\ell_0$, $\ell_1$, and $\ell_\infty$-norm attacks in terms of perturbation size, convergence speed and computation time, while reporting comparable performances with state-of-the-art $\ell_2$-norm attacks. Our open-source code is available at: https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack.

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named “trit” (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the “no-anomaly” one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment.