Christophe Hauser

Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis et al. · cambridge, microsoft-research

Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. The semantic layout is used to break down the final reasoning decision into a series of lower-level decisions. We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.

Shushan Arakelyan, Sima Arasteh, Christophe Hauser et al.

Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time-consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks - functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code-based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

Ronny Chevalier, Stefano Cristalli, Christophe Hauser et al.

Boot firmware, like UEFI-compliant firmware, has been the target of numerous attacks, giving the attacker control over the entire system while being undetected. The measured boot mechanism of a computer platform ensures its integrity by using cryptographic measurements to detect such attacks. This is typically performed by relying on a Trusted Platform Module (TPM). Recent work, however, shows that vendors do not respect the specifications that have been devised to ensure the integrity of the firmware's loading process. As a result, attackers may bypass such measurement mechanisms and successfully load a modified firmware image while remaining unnoticed. In this paper we introduce BootKeeper, a static analysis approach verifying a set of key security properties on boot firmware images before deployment, to ensure the integrity of the measured boot process. We evaluate BootKeeper against several attacks on common boot firmware implementations and demonstrate its applicability.