Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts

Diffusion models have significantly advanced text-to-image generation, enabling the creation of highly realistic images conditioned on textual prompts and seeds. Given the considerable intellectual and economic value embedded in such prompts, prompt theft poses a critical security and privacy concern. In this paper, we investigate prompt-stealing attacks targeting diffusion models. We reveal that numerical optimization-based prompt recovery methods are fundamentally limited as they do not account for the initial random noise used during image generation. We identify and exploit a noise-generation vulnerability (CWE-339), prevalent in major image-generation frameworks, originating from PyTorch's restriction of seed values to a range of $2^{32}$ when generating the initial random noise on CPUs. Through a large-scale empirical analysis conducted on images shared via the popular platform CivitAI, we demonstrate that approximately 95% of these images' seed values can be effectively brute-forced in 140 minutes per seed using our seed-recovery tool, SeedSnitch. Leveraging the recovered seed, we propose PromptPirate, a genetic algorithm-based optimization method explicitly designed for prompt stealing. PromptPirate surpasses state-of-the-art methods, i.e., PromptStealer, P2HP, and CLIP-Interrogator, achieving an 8-11% improvement in LPIPS similarity. Furthermore, we introduce straightforward and effective countermeasures that render seed stealing, and thus optimization-based prompt stealing, ineffective. We have disclosed our findings responsibly and initiated coordinated mitigation efforts with the developers to address this critical vulnerability.

AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution

Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs. In this work, we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programming. When the symbolic executor encounters an external function, AutoStub generates training data by executing the function on randomly generated inputs and collecting the outputs. Genetic Programming then derives expressions that approximate the behavior of the function, serving as symbolic stubs. These automatically generated stubs allow the symbolic executor to continue the analysis without manual intervention, enabling the exploration of program paths that were previously intractable. We demonstrate that AutoStub can automatically approximate external functions with over 90% accuracy for 55% of the functions evaluated, and can infer language-specific behaviors that reveal edge cases crucial for software testing.

Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction

As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine learning-based approaches are promising, they typically perform well only in scenarios where training and test data are closely related. A key challenge for ML-based vulnerability detection is providing suitable and concise code context, as excessively long contexts negatively affect the code comprehension capabilities of machine learning models, particularly smaller ones. This work introduces Trace Gadgets, a novel code representation that minimizes code context by removing non-related code. Trace Gadgets precisely capture the statements that cover the path to the vulnerability. As input for ML models, Trace Gadgets provide a minimal but complete context, thereby improving the detection performance. Moreover, we collect a large-scale dataset generated from real-world applications with manually curated labels to further improve the performance of ML-based vulnerability detectors. Our results show that state-of-the-art machine learning models perform best when using Trace Gadgets compared to previous code representations, surpassing the detection capabilities of industry-standard static scanners such as GitHub's CodeQL by at least 4% on a fully unseen dataset. By applying our framework to real-world applications, we identify and report previously unknown vulnerabilities in widely deployed software.

OCEAN: Open-World Contrastive Authorship Identification

In an era where cyberattacks increasingly target the software supply chain, the ability to accurately attribute code authorship in binary files is critical to improving cybersecurity measures. We propose OCEAN, a contrastive learning-based system for function-level authorship attribution. OCEAN is the first framework to explore code authorship attribution on compiled binaries in an open-world and extreme scenario, where two code samples from unknown authors are compared to determine if they are developed by the same author. To evaluate OCEAN, we introduce new realistic datasets: CONAN, to improve the performance of authorship attribution systems in real-world use cases, and SNOOPY, to increase the robustness of the evaluation of such systems. We use CONAN to train our model and evaluate on SNOOPY, a fully unseen dataset, resulting in an AUROC score of 0.86 even when using high compiler optimizations. We further show that CONAN improves performance by 7% compared to the previously used Google Code Jam dataset. Additionally, OCEAN outperforms previous methods in their settings, achieving a 10% improvement over state-of-the-art SCS-Gan in scenarios analyzing source code. Furthermore, OCEAN can detect code injections from an unknown author in a software update, underscoring its value for securing software supply chains.