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.

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.

DASH: Accelerating Distributed Private Machine Learning Inference with Arithmetic Garbled Circuits

The adoption of machine learning solutions is rapidly increasing across all parts of society. Cloud service providers such as Amazon Web Services, Microsoft Azure and the Google Cloud Platform aggressively expand their Machine-Learning-as-a-Service offerings. While the widespread adoption of machine learning has huge potential for both research and industry, the large-scale evaluation of possibly sensitive data on untrusted platforms bears inherent data security and privacy risks. Since computation time is expensive, performance is a critical factor for machine learning. However, prevailing security measures proposed in the past years come with a significant performance overhead. We investigate the current state of protected distributed machine learning systems, focusing on deep convolutional neural networks. The most common and best-performing mixed MPC approaches are based on homomorphic encryption, secret sharing, and garbled circuits. They commonly suffer from communication overheads that grow linearly in the depth of the neural network. We present Dash, a fast and distributed private machine learning inference scheme. Dash is based purely on arithmetic garbled circuits. It requires only a single communication round per inference step, regardless of the depth of the neural network, and a very small constant communication volume. Dash thus significantly reduces performance requirements and scales better than previous approaches. In addition, we introduce the concept of LabelTensors. This allows us to efficiently use GPUs while using garbled circuits, which further reduces the runtime. Dash offers security against a malicious attacker and is up to 140 times faster than previous arithmetic garbling schemes.