Aayush Garg

Aayush Garg, Renzo Degiovanni, Matthieu Jimenez et al.

Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the problem, and b) the inherently noisy historical data, i.e., most vulnerabilities are discovered much later than they are introduced. This misleads classifiers as they learn to recognize actual vulnerable components as non-vulnerable. To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed. We perform this by contrasting the known vulnerable, and their respective fixed components. This way, TROVON manages to learn from the things we know, i.e., vulnerabilities, hence reducing the effects of noisy and unbalanced data. We evaluate TROVON by comparing it with existing techniques on three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and Wireshark, with historical vulnerabilities that have been reported in the National Vulnerability Database (NVD). Our evaluation demonstrates that the prediction capability of TROVON significantly outperforms existing vulnerability prediction techniques such as Software Metrics, Imports, Function Calls, Text Mining, Devign, LSTM, and LSTM-RF with an improvement of 40.84% in Matthews Correlation Coefficient (MCC) score under Clean Training Data Settings, and an improvement of 35.52% under Realistic Training Data Settings.

Aayush Garg, Orlando Amaral Cejas

Mobile core networks rely on several signalling protocols in parallel, such as SS7, Diameter, and GTP, so many security-relevant problems become visible only when their interactions are analyzed jointly. At the same time, labeled examples of real attacks and cross-protocol misconfigurations are scarce, which complicates supervised detection. We therefore study unsupervised cross-protocol anomaly analysis on fused representations that combine SS7, Diameter, and GTP signalling. For each subscriber, we aggregate messages into per-minute fused records, serialize each record as text, embed it with several models, and apply unsupervised anomaly detection. We then assign each record a consensus score equal to the number of embedding models that flag it as anomalous. For evaluation, we generate cross-protocol-plausible synthetic anomalies by swapping one field group at a time between pairs of records, preserving per-message validity while making the fused view contradictory. On 219,294 fused records, 44.15% are flagged by at least one model, but only 0.97% reach full agreement across all six. Higher consensus is strongly associated with synthetic records, where for k=1-4 the odds that a flagged record is synthetic are hundreds of times greater than for original records, and for k>=5 all flagged records are synthetic, with extremely small p-values. Cosine distances between synthetic and original records also increase with consensus, suggesting clearer separation in embedding space. These results support the use of multi-embedding consensus to prioritize a much smaller set of candidate cross-protocol inconsistencies for further inspection.

Aayush Garg, Zanis Ali Khan, Renzo Degiovanni et al.

Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising capabilities for automating this task. However, existing research has primarily assessed LLMs using publicly disclosed vulnerabilities, leaving their effectiveness on related artificial vulnerabilities largely unexplored. In this study, we empirically evaluate the patching effectiveness and complementarity of several prominent LLMs, such as OpenAI's GPT variants, LLaMA, DeepSeek, and Mistral models, using both real and artificial vulnerabilities. Our evaluation employs Proof-of-Vulnerability (PoV) test execution to concretely assess whether LLM-generated source code successfully patches vulnerabilities. Our results reveal that LLMs patch real vulnerabilities more effectively compared to artificial ones. Additionally, our analysis reveals significant variability across LLMs in terms of overlapping (multiple LLMs patching the same vulnerabilities) and complementarity (vulnerabilities patched exclusively by a single LLM), emphasizing the importance of selecting appropriate LLMs for effective vulnerability patching.

Zanis Ali Khan, Aayush Garg, Qiang Tang

Software vulnerabilities pose significant security threats, requiring effective mitigation. While Automated Program Repair (APR) has advanced in fixing general bugs, vulnerability patching, a security-critical aspect of APR remains underexplored. This study investigates pre-trained language models, CodeBERT and CodeT5, for automated vulnerability patching across six datasets and four languages. We evaluate their accuracy and generalization to unknown vulnerabilities. Results show that while both models face challenges with fragmented or sparse context, CodeBERT performs comparatively better in such scenarios, whereas CodeT5 excels in capturing complex vulnerability patterns. CodeT5 also demonstrates superior scalability. Furthermore, we test fine-tuned models on both in-distribution (trained) and out-of-distribution (unseen) datasets. While fine-tuning improves in-distribution performance, models struggle to generalize to unseen data, highlighting challenges in robust vulnerability detection. This study benchmarks model performance, identifies limitations in generalization, and provides actionable insights to advance automated vulnerability patching for real-world security applications.

Milos Ojdanic, Aayush Garg, Ahmed Khanfir et al.

Fault seeding is typically used in controlled studies to evaluate and compare test techniques. Central to these techniques lies the hypothesis that artificially seeded faults involve some form of realistic properties and thus provide realistic experimental results. In an attempt to strengthen realism, a recent line of research uses advanced machine learning techniques, such as deep learning and Natural Language Processing (NLP), to seed faults that look like (syntactically) real ones, implying that fault realism is related to syntactic similarity. This raises the question of whether seeding syntactically similar faults indeed results in semantically similar faults and more generally whether syntactically dissimilar faults are far away (semantically) from the real ones. We answer this question by employing 4 fault-seeding techniques (PiTest - a popular mutation testing tool, IBIR - a tool with manually crafted fault patterns, DeepMutation - a learning-based fault seeded framework and CodeBERT - a novel mutation testing tool that use code embeddings) and demonstrate that syntactic similarity does not reflect semantic similarity. We also show that 60%, 47%, 43%, and 7% of the real faults of Defects4J V2 are semantically resembled by CodeBERT, PiTest, IBIR, and DeepMutation faults. We then perform an objective comparison between the techniques and find that CodeBERT and PiTest have similar fault detection capabilities that subsume IBIR and DeepMutation, and that IBIR is the most cost-effective technique. Moreover, the overall fault detection of PiTest, CodeBERT, IBIR, and DeepMutation was, on average, 54%, 53%, 37%, and 7%.

Aayush Garg, Milos Ojdanic, Renzo Degiovanni et al.

Mutation testing research has indicated that a major part of its application cost is due to the large number of low utility mutants that it introduces. Although previous research has identified this issue, no previous study has proposed any effective solution to the problem. Thus, it remains unclear how to mutate and test a given piece of code in a best effort way, i.e., achieving a good trade-off between invested effort and test effectiveness. To achieve this, we propose Cerebro, a machine learning approach that statically selects subsuming mutants, i.e., the set of mutants that resides on the top of the subsumption hierarchy, based on the mutants' surrounding code context. We evaluate Cerebro using 48 and 10 programs written in C and Java, respectively, and demonstrate that it preserves the mutation testing benefits while limiting application cost, i.e., reduces all cost application factors such as equivalent mutants, mutant executions, and the mutants requiring analysis. We demonstrate that Cerebro has strong inter-project prediction ability, which is significantly higher than two baseline methods, i.e., supervised learning on features proposed by state-of-the-art, and random mutant selection. More importantly, our results show that Cerebro's selected mutants lead to strong tests that are respectively capable of killing 2 times higher than the number of subsuming mutants killed by the baselines when selecting the same number of mutants. At the same time, Cerebro reduces the cost-related factors, as it selects, on average, 68% fewer equivalent mutants, while requiring 90% fewer test executions than the baselines.

Aayush Garg, Thilo Will, William Darling et al.

Following recent breakthroughs in convolutional neural networks and monolithic model architectures, state-of-the-art object detection models can reliably and accurately scale into the realm of up to thousands of classes. Things quickly break down, however, when scaling into the tens of thousands, or, eventually, to millions or billions of unique objects. Further, bounding box-trained end-to-end models require extensive training data. Even though - with some tricks using hierarchies - one can sometimes scale up to thousands of classes, the labor requirements for clean image annotations quickly get out of control. In this paper, we present a two-layer object detection method for brand logos and other stylized objects for which prototypical images exist. It can scale to large numbers of unique classes. Our first layer is a CNN from the Single Shot Multibox Detector family of models that learns to propose regions where some stylized object is likely to appear. The contents of a proposed bounding box is then run against an image index that is targeted for the retrieval task at hand. The proposed architecture scales to a large number of object classes, allows to continously add new classes without retraining, and exhibits state-of-the-art quality on a stylized object detection task such as logo recognition.