We introduce audience injection attacks, a novel class of vulnerabilities that impact widely used Web-based authentication and authorization protocol standards, including OAuth 2.0, OpenID Connect, FAPI, CIBA, the Device Authorization Grant, and various well-established extensions, such as Pushed Authorization Requests, Token Revocation, Token Introspection, and their numerous combinations. These protocols underpin services for billions of users across diverse ecosystems worldwide, spanning low-risk applications like social logins to high-risk domains such as open banking, insurance, and healthcare. Audience injection attacks exploit a critical weakness in a core security mechanism of these standards - the handling of so-called audiences in signature-based client authentication mechanisms. This vulnerability allows attackers to compromise fundamental security objectives whenever these mechanisms are utilized across two or more server endpoints. They enable the attacker to impersonate users and gain unauthorized access to their resources, even in high-security protocol families specifically designed for sensitive applications. We responsibly disclosed these vulnerabilities to the relevant standardization bodies, which recognized their severity. In collaboration with these organizations, we developed fixes and supported a coordinated response, leading to an ongoing effort to update a dozen standards.
Provenance-based Intrusion Detection Systems (PIDSes) have become one of the most promising solutions for detecting sophisticated attacks. However, many existing PIDSes can be evaded by approaches obfuscating malicious nodes among frequent benign events (i.e., gadgets) or replacing rare malicious events with frequent benign sequences. While effective against earlier PIDSes, those approaches largely overlook advanced node-level PIDSes, which leverage semantic information, temporal ordering, and graph representation learning to model the full context of nodes. This enriched view makes evasions that rely only on frequency of events less effective, as malicious nodes can still be distinguished by their semantic and structural relationships. Based on such an observation, we propose Contorter, an evasion framework that leverages the enhanced embeddings of node-level PIDSes to guide gadget generation and uncovers their blind spots for improving robustness. Specifically, Contorter identifies benign nodes of the same entity type as each malicious node, and selects one with a similar context that is labeled benign with high confidence by the target PIDS. It then replicates edges of the selected node around the malicious node to further align their contexts and hide the latter from the PIDS. We implement and evaluate Contorter on Darpa E3, OpTC, Unicorn, and StreamSpot datasets, and based on four exemplar node-level PIDSes. The results show that Contorter can reduce the recall to as low as 0 % (average 59 % reduction), while keeping the false positive rate mostly unchanged to avoid suspicion. Finally, compared to prior approaches, Contorter achieves over a 35 % greater reduction in recall under the same assumptions, while it requires about seven times fewer added edges.
We present InsPIRe that is the first private information retrieval (PIR) construction simultaneously obtaining both high-throughput and low query communication while using only server-side preprocessing (meaning no offline communication). Prior PIR schemes with both high-throughput and low query communication required substantial offline communication of either downloading a database hint that is $10-100 \mathrm{x}$ larger than the communication cost of a single query (such as SimplePIR and DoublePIR [Henzinger et al., USENIX Security 2023]) or streaming the entire database (such as Piano [Zhou et al., S&P 2024]). In contrast, recent works such as YPIR [Menon and Wu, USENIX Security 2024] avoid offline communication at the cost of increasing the query size by $1.8-2 \mathrm{x}$, up to $1-2 \text{MB}$ per query. Our new PIR protocol, InsPIRe, obtains the best of both worlds by obtaining high-throughput and low communication without requiring any offline communication. Compared to YPIR, InsPIRe requires 5x smaller cryptographic keys, requires up to 50 % less online query communication while obtaining up to 25 % higher throughput. We show that InsPIRe enables improvements across a wide range of applications and database shapes including the InterPlanetary File System and private device enrollment. At the core of InsPIR, we develop a novel ring packing algorithm, InspiRING, for transforming LWE ciphertexts into RLWE ciphertexts. InspiRING is more amenable to the server-side preprocessing setting that allows moving the majority of the necessary operations to offline preprocessing. InspiRING only requires two key-switching matrices whereas prior approaches needed logarithmic key-switching matrices. We also show that InspiRING has smaller noise growth and faster packing times than prior works in the setting when the total key-switching material sizes must be small. To further reduce communication costs in the PIR protocol, InsPIRe performs the second level of PIR using homomorphic polynomial evaluation, which only requires one additional ciphertext from the client.
Despite decades of research, memory safety solutions see limited adoption, as they often incur high overheads, are complex to deploy, or cover only a narrow scope of bugs. In this paper, we present QuickSafe — a targeted approach to harden programs against exploitation of known but unresolved memory errors with minimal overhead. QuickSafe yields a stopgap patch that is immediately available, while the bug awaits eventual resolution. Where most existing automatic patch generators rely on inserting runtime constraint checks in the code to stop exploits, QuickSafe instead isolates memory objects associated with a known bug from the rest of the program. Object isolation can be implemented in different ways, depending on hardware support and desired security guarantees. To assess the viability, we present two such implementations. On traditional architectures, we allocate vulnerable objects on dedicated pages flanked by inaccessible guard pages. On platforms that support Memory Tagging Extensions (MTE), we offer stronger guarantees by enforcing disjoint tag domains. To reliably identify the objects associated with a given memory error, we introduce TagASan — an extension of AddressSanitizer (ASan) that uses tagged pointers to trace faulting accesses back to their originating allocation sites. As an additional contribution, we present a new dataset of 223 real-world memory errors across ten prominent projects to measure the performance of automatic patch generators. We evaluate QuickSafe on (1) this new benchmark suite, (2) the Juliet Test Suite, and (3) buggy benchmarks from SPEC CPU2006/2017. Using the guard-page-based isolation backend, QuickSafe protects against the exploitation of all evaluated bugs, incurring a geomean memory overhead of 2.46 % and a geomean runtime overhead of 2.67 % - with the vast majority of applications slowing down by only around 1 %. We apply the MTE-based isolation strategy to a representative subset of the dataset, confirming its effectiveness and showing negligible runtime overhead of $\approx 0.12 \%$.
To promote shared learning and accountability around differential privacy (DP) deployments, Dwork, Kohli, and Mulligan [1] proposed a public-facing repository (“registry”) of real-world DP deployments. The DP community has recently started working toward realizing this vision. We contribute to this effort by (1) developing a holistic, hierarchical schema to describe any given DP deployment and (2) designing and implementing an interactive interface to act as a registry where practitioners can access information about past DP deployments. We (3) populate our interface with 21 real-world DP deployments and (4) conduct an exploratory user study with DP practitioners ($\mathbf{n} \boldsymbol{=} \mathbf{1 6}$) to understand how they would use the registry, as well as what challenges and opportunities they foresee around its adoption. We find that participants were enthusiastic about the registry as a valuable resource for evaluating prior deployments and making future deployments. They also identified several opportunities for the registry, including that it can become a “hub” for the community and support broader communication around DP (e.g., to legal teams). At the same time, they identified challenges around the registry gaining adoption, including the effort and risk involved with making implementation choices public and moderating the quality of entries. Based on our findings, we offer recommendations for encouraging adoption and increasing the registry's value not only to DP practitioners, but also to policymakers, data users, and data subjects.
