Hardware-assisted Control-flow Integrity (CFI), such as Intel’s Indirect Branch Tracking (IBT), provides an efficient defense against control-flow hijacking attacks. However, the protection it provides is fundamentally coarse-grained, leaving a large attack surface of valid indirect branch targets that an attacker can (ab)use. Recently, FineIBT introduced a novel, load-time feature called NOPout that aims to reduce this attack surface by removing endbr instructions from unused, exported functions. Unfortunately, NOPout suffers from several design limitations—including insecure fail-safe defaults, performance overhead from excessive code patching and copy-on-write (COW) memory effects, and a cursory design for dynamically loaded libraries—as well as an evaluation narrow in scope. To address the shortcomings of NOPout, we present NOPoutNG: a novel mechanism that reverses NOPout’s insecure model with a secure-by-default approach. It begins by inserting inert nop instructions into exported functions, only promoting them to active endbr "landing pads" when there is concrete evidence—discovered at compile, link, or load time—that a function is actually used (i.e., address-taken). We implement NOPoutNG as a collection of minor modifications to the LLVM toolchain, as well as a load-time shared library, and demonstrate its practicality and security via a comprehensive evaluation where it achieves up to 86% endbr reduction vs. vanilla IBT with minimal overhead.
Measured Boot, a mechanism enabled through Trusted Platform Modules (TPMs), is commonly used for pass-wordless protection of data-at-rest, aiming to protect data when the device is lost or stolen. Microsoft’s standpoint is neutral on which way a TPM should be implemented: Firmware-based TPMs (fTPMs) are viewed as more economical but less secure. Despite the inherent susceptibility to bus sniffing attacks, discrete TPMs (dTPMs) are still seen as the gold standard, as many deliver better on-paper tamper resistance. It is often argued that attacks against the bus can be mitigated by bus encryption and, ideally, mutual authentication between the CPU and TPM.This position paper aims to emphasize another inherent, difficult-to-mitigate attack against dTPMs that was originally shown against a TPM 1.1 over 20 years ago: We demonstrate that even brief physical access to a TPM 2.0 and the ability to boot from an attacker-controlled system enable an attacker to reset and replay arbitrary measurements, thereby allowing an attacker to unseal, for example, a disk encryption key solely protected by the TPM. While there have been attacks against fTPMs, too, we argue that their practical attack surface is fundamentally smaller. Bus protection techniques can be used to protect dTPMs, but only guard against passive attacks. After all, we argue that, from a security standpoint, firmware TPMs, or any TPM internal to the SoC, are superior to discrete (external) ones. Lastly, in order for dTPM-based setups to provide meaningful protection of sealed secrets, configurations must require a user-provided PIN or password along with the Measured Boot configuration.
Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning where adversaries inject malicious documents to corrupt outputs. We introduce Proof-Carrying Answers (PCA), a protocol requiring cryptographic receipts for every claim before answer release. PCA combines systematic admissibility contracts, Merkle-authenticated vector indexes with Ed25519 signatures, and threat-model-driven evaluation. Experiments on WikiText-103 show PCA reduces poisoned outputs by 47% at 10% corpus poisoning while maintaining 65% answer coverage with only 1.2% overhead. We establish PCA as necessary defense alongside semantic verification.
The growing scale and heterogeneity of Internet of Things (IoT) deployments have amplified the security impact of zero-day attacks. These exploits target previously unknown vulnerabilities, bypassing signature-based intrusion detection systems. Machine learning offers a promising alternative through anomaly detection, but systematic guidance for model selection under real-world constraints remains limited. This paper presents a comprehensive benchmark of ten anomaly detection architectures across four machine learning paradigms on the large-scale CICIoT2023 dataset (46.8 M flows, 33 attack types). All models are trained using benign-only data to emulate zero-day scenarios and evaluated using both detection and efficiency metrics on simulated edge hardware. The results reveal a clear performance–efficiency Pareto frontier. Long Short-Term Memory Autoencoders achieve the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) (99.58 %) but incur significant computational cost. Lightweight autoencoders provide near-optimal detection (99.44 %) with orders-of-magnitude lower resource use, while classical tree-based methods offer speed but miss most attacks. These findings establish baselines for IoT zero-day detection and offer evidence-based recommendations for edge, gateway, and cloud deployments.
The fifth generation (5G) of wireless communication offers gigabit-per-second speeds, ultra-low latency, and massive device connectivity. However, the introduction of network function virtualization (NFV), software-defined networking (SDN), network slicing, and mobile edge computing (MEC) has also created a complex security frontier. Addressing these challenges requires continuous and realistic validation of defenses in controlled experimentation environments. In this paper, we present an open-source, low-cost, fully software-based 5G testbed designed as a cyber range platform for security research and education. Built upon the OpenAirInterface (OAI) framework and commercial off-the-shelf hardware, the testbed supports an end-to-end 5G Stand-Alone (SA) deployment with gNodeB, core network, and user equipment (UE). It enables reproducible experimentation that may demonstrate a broad range of security scenarios, including privacy and tracking-based vulnerabilities like capturing user identifiers, as well as availability attacks such as Denial of Service (DoS) attacks targeting core network functions. The paper outlines our reproducible implementation and shares our experiences in building and operating the platform as a cyber range.
