Vulnerability Detection with Code Language Models: How Far Are We?

In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our analysis reveals significant shortcomings in existing vulnerability datasets, including poor data quality, low label accuracy, and high duplication rates, leading to unreliable model performance in realistic vulnerability detection scenarios. Additionally, the evaluation methods used with these datasets are not representative of real-world vulnerability detection. To address these challenges, we introduce PrimeVul, a new dataset for training and evaluating code LMs for vulnerability detection. PrimeVul incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks while significantly expanding the dataset. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues, alongside introducing more realistic evaluation metrics and settings. This comprehensive approach aims to provide a more accurate assessment of code LMs' performance in real-world conditions. Evaluating code LMs on PrimeVul reveals that existing benchmarks significantly overestimate the performance of these models. For instance, a state-of-the-art 7B model scored 68.26% F1 on BigVul but only 3.09% F1 on PrimeVul. Attempts to improve performance through advanced training techniques and larger models like GPT-3.5 and GPT-4 were unsuccessful, with results akin to random guessing in the most stringent settings. These findings underscore the considerable gap between current capabilities and the practical requirements for deploying code LMs in security roles, highlighting the need for more innovative research in this domain.

2L3: Lifting Imperfect Generated 2D Images into Accurate 3D

Reconstructing 3D objects from a single image is an intriguing but challenging problem. One promising solution is to utilize multi-view (MV) 3D reconstruction to fuse generated MV images into consistent 3D objects. However, the generated images usually suffer from inconsistent lighting, misaligned geometry, and sparse views, leading to poor reconstruction quality. To cope with these problems, we present a novel 3D reconstruction framework that leverages intrinsic decomposition guidance, transient-mono prior guidance, and view augmentation to cope with the three issues, respectively. Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures. Our approach, therefore, enables the integration of a pre-trained MV image generator and a neural network-based volumetric signed distance function (SDF) representation for a single image to 3D object reconstruction. We evaluate our framework on various datasets and demonstrate its superior performance in both quantitative and qualitative assessments, signifying a significant advancement in 3D object reconstruction. Compared with the latest state-of-the-art method Syncdreamer~\cite{liu2023syncdreamer}, we reduce the Chamfer Distance error by about 36\% and improve PSNR by about 30\% .

DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 150 CWEs, 26,635 vulnerable functions, and 352,606 non-vulnerable functions extracted from 7,861 commits. Our dataset covers 305 more projects than all previous datasets combined. We show that increasing the diversity and volume of training data improves the performance of deep learning models for vulnerability detection. Combining our new dataset with previous datasets, we present an analysis of the challenges and promising research directions of using deep learning for detecting software vulnerabilities. We study 11 model architectures belonging to 4 families. Our results show that deep learning is still not ready for vulnerability detection, due to high false positive rate, low F1 score, and difficulty of detecting hard CWEs. In particular, we demonstrate an important generalization challenge for the deployment of deep learning-based models. However, we also identify hopeful future research directions. We demonstrate that large language models (LLMs) are the future for vulnerability detection, outperforming Graph Neural Networks (GNNs) with manual feature engineering. Moreover, developing source code specific pre-training objectives is a promising research direction to improve the vulnerability detection performance.

Continuous Learning for Android Malware Detection

Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps. Our research finds that, after training an Android malware classifier on one year's worth of data, the F1 score quickly dropped from 0.99 to 0.76 after 6 months of deployment on new test samples. In this paper, we propose new methods to combat the concept drift problem of Android malware classifiers. Since machine learning technique needs to be continuously deployed, we use active learning: we select new samples for analysts to label, and then add the labeled samples to the training set to retrain the classifier. Our key idea is, similarity-based uncertainty is more robust against concept drift. Therefore, we combine contrastive learning with active learning. We propose a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier. Our evaluation shows that this leads to significant improvements, compared to previously published methods for active learning. Our approach reduces the false negative rate from 16% (for the best baseline) to 10%, while maintaining the same false positive rate (0.6%). Also, our approach maintains more consistent performance across a seven-year time period than past methods.

Part-Based Models Improve Adversarial Robustness

We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks by introducing a part-based model for object classification. We believe that the richer form of annotation helps guide neural networks to learn more robust features without requiring more samples or larger models. Our model combines a part segmentation model with a tiny classifier and is trained end-to-end to simultaneously segment objects into parts and then classify the segmented object. Empirically, our part-based models achieve both higher accuracy and higher adversarial robustness than a ResNet-50 baseline on all three datasets. For instance, the clean accuracy of our part models is up to 15 percentage points higher than the baseline's, given the same level of robustness. Our experiments indicate that these models also reduce texture bias and yield better robustness against common corruptions and spurious correlations. The code is publicly available at https://github.com/chawins/adv-part-model.

Learning Security Classifiers with Verified Global Robustness Properties

Recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all inputs. Since data distribution shift is very common in security applications, e.g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier. Therefore, it is more desirable to enforce global robustness properties that hold for all inputs, which is strictly stronger than local robustness. In this paper, we present a framework and tools for training classifiers that satisfy global robustness properties. We define new notions of global robustness that are more suitable for security classifiers. We design a novel booster-fixer training framework to enforce global robustness properties. We structure our classifier as an ensemble of logic rules and design a new verifier to verify the properties. In our training algorithm, the booster increases the classifier's capacity, and the fixer enforces verified global robustness properties following counterexample guided inductive synthesis. To the best of our knowledge, the only global robustness property that has been previously achieved is monotonicity. Several previous works have defined global robustness properties, but their training techniques failed to achieve verified global robustness. In comparison, we show that we can train classifiers to satisfy different global robustness properties for three security datasets, and even multiple properties at the same time, with modest impact on the classifier's performance. For example, we train a Twitter spam account classifier to satisfy five global robustness properties, with 5.4% decrease in true positive rate, and 0.1% increase in false positive rate, compared to a baseline XGBoost model that doesn't satisfy any property.

Training Robust Tree Ensembles for Security

Tree ensemble models including random forests and gradient boosted decision trees, are widely used as security classifiers to detect malware, phishing, scam, social engineering, etc. However, the robustness of tree ensembles has not been thoroughly studied. Existing approaches mainly focus on adding more robust features and conducting feature ablation study, which do not provide robustness guarantee against strong adversaries. In this paper, we propose a new algorithm to train robust tree ensembles. Robust training maximizes the defender's gain as if the adversary is trying to minimize that. We design a general algorithm based on greedy heuristic to find better solutions to the minimization problem than previous work. We implement the algorithm for gradient boosted decision trees in xgboost and random forests in scikit-learn. Our evaluation over benchmark datasets show that, we can train more robust models than the start-of-the-art robust training algorithm in gradient boosted decision trees, with a 1.26X increase in the $L_\infty$ evasion distance required for the strongest whitebox attacker. In addition, our algorithm is general across different gain metrics and types of tree ensembles. We achieve 3.32X increase in $L_\infty$ robustness distance compared to the baseline random forest training method. Furthermore, to make the robustness increase meaningful in security applications, we propose attack-cost-driven constraints for the robust training process. Our training algorithm maximizes attacker's evasion cost by integrating domain knowledge about feature manipulation costs. We use twitter spam detection as a case study to analyze attacker's cost increase to evade our robust model. Our technique can train robust model to rank robust features as most important ones, and our robust model requires about 8.4X increase in attacker's economic cost to be evaded compared to the baseline.

Enhancing Gradient-based Attacks with Symbolic Intervals

Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gradient-based attacks). However, it is an open question whether the adversarially trained networks are truly robust under unknown attacks. In this paper, we present interval attacks, a new technique to find adversarial examples to evaluate the robustness of neural networks. Interval attacks leverage symbolic interval propagation, a bound propagation technique that can exploit a broader view around the current input to locate promising areas containing adversarial instances, which in turn can be searched with existing gradient-guided attacks. We can obtain such a broader view using sound bound propagation methods to track and over-approximate the errors of the network within given input ranges. Our results show that, on state-of-the-art adversarially trained networks, interval attack can find on average 47% relatively more violations than the state-of-the-art gradient-guided PGD attack.

On Training Robust PDF Malware Classifiers

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the computation cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 99% verified robust accuracy, while maintaining 99.80% accuracy and 0.41% false positive rate. With simple robustness properties, the state-of-the-art unbounded attacker found no successful evasion on the robust classifier in 6 hours. Even for a new unbounded adaptive attacker we have designed, the number of successful evasions within a fixed time budget is cut down by 4x.

MixTrain: Scalable Training of Verifiably Robust Neural Networks

Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training and verifiably robust training, have limitations that restrict their practical applications. The adversarially robust training only makes the networks robust against a subclass of attackers and we reveal such weaknesses by developing a new attack based on interval gradients. By contrast, verifiably robust training provides protection against any L-p norm-bounded attacker but incurs orders of magnitude more computational and memory overhead than adversarially robust training. We propose two novel techniques, stochastic robust approximation and dynamic mixed training, to drastically improve the efficiency of verifiably robust training without sacrificing verified robustness. We leverage two critical insights: (1) instead of over the entire training set, sound over-approximations over randomly subsampled training data points are sufficient for efficiently guiding the robust training process; and (2) We observe that the test accuracy and verifiable robustness often conflict after certain training epochs. Therefore, we use a dynamic loss function to adaptively balance them for each epoch. We designed and implemented our techniques as part of MixTrain and evaluated it on six networks trained on three popular datasets including MNIST, CIFAR, and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\%$ verified robust accuracy against $L_\infty$ norm-bounded attackers while taking $15$ and $3$ times less training time than state-of-the-art verifiably robust training and adversarially robust training schemes, respectively. Furthermore, MixTrain easily scales to larger networks like the one trained on ImageNet-200, significantly outperforming the existing verifiably robust training methods.