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.

Shenghan Zheng, Qifan Zhang, Zheng Zhang et al.

AI agent protocols define how agents use tools, delegate work, and coordinate across software systems, but their security requirements remain incomplete and inconsistently enforced across deployments. We present AgentThread, a source-linked framework for security assurance analysis of agent protocols, from specification text to running SDKs. AgentThread contributes a layered security scope, protocol-derived checks formalized as TLA+ invariants, and a two-phase checker that compiles protocol specifications into model-checkable models and replays executable counterexamples against real SDKs through protocol adapters. For each finding, AgentThread records the source text behind the check and separates violated protocol requirements from missing recommendations, hardening gaps, and unassigned cross-protocol responsibilities. Across five emerging agent protocols, AgentThread identifies 35 specification-level findings, supports them with 80 implementation tests against production SDKs and reference servers, and finds 30 additional failures that emerge only under protocol composition. We further show that only one protocol enforces a security-relevant control in practice and no protocol assigns enforcement for cross-protocol behavior. Insecurity in agent protocols is therefore not only a specification or implementation problem, but also a responsibility gap across protocols, SDKs, and deployments.

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).