arXiv subjects

Jean-Pierre Seifert

Publications and source records attributed to Jean-Pierre Seifert.

At least 19 recordsLinked to original sources

arXiv2026

Not Discrete Enough: On the Inherent Insecurity of dTPMs for Measured Boot

Measured Boot, a mechanism enabled through Trusted Platform Modules (TPMs), is commonly used for passwordless 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.

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arXiv2024

Good Gottesman-Kitaev-Preskill codes from the NTRU cryptosystem

We introduce a new class of random Gottesman-Kitaev-Preskill (GKP) codes derived from the cryptanalysis of the so-called NTRU cryptosystem. The derived codes are good in that they exhibit constant rate and average distance scaling $Δ\propto \sqrt{n}$ with high probability, where $n$ is the number of bosonic modes, which is a distance scaling equivalent to that of a GKP code obtained by concatenating single mode GKP codes into a qubit-quantum error correcting code with linear distance. The derived class of NTRU-GKP codes has the additional property that decoding for a stochastic displacement noise model is equivalent to decrypting the NTRU cryptosystem, such that every random instance of the code naturally comes with an efficient decoder. This construction highlights how the GKP code bridges aspects of classical error correction, quantum error correction as well as post-quantum cryptography. We underscore this connection by discussing the computational hardness of decoding GKP codes and propose, as a new application, a simple public key quantum communication protocol with security inherited from the NTRU cryptosystem.

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arXiv2024

What All the PHUZZ Is About: A Coverage-guided Fuzzer for Finding Vulnerabilities in PHP Web Applications

Coverage-guided fuzz testing has received significant attention from the research community, with a strong focus on binary applications, greatly disregarding other targets, such as web applications. The importance of the World Wide Web in everyone's life cannot be overstated, and to this day, many web applications are developed in PHP. In this work, we address the challenges of applying coverage-guided fuzzing to PHP web applications and introduce PHUZZ, a modular fuzzing framework for PHP web applications. PHUZZ uses novel approaches to detect more client-side and server-side vulnerability classes than state-of-the-art related work, including SQL injections, remote command injections, insecure deserialization, path traversal, external entity injection, cross-site scripting, and open redirection. We evaluate PHUZZ on a diverse set of artificial and real-world web applications with known and unknown vulnerabilities, and compare it against a variety of state-of-the-art fuzzers. In order to show PHUZZ' effectiveness, we fuzz over 1,000 API endpoints of the 115 most popular WordPress plugins, resulting in over 20 security issues and 2 new CVE-IDs. Finally, we make the framework publicly available to motivate and encourage further research on web application fuzz testing.

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arXiv2024

An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory

Combinatorial optimization - a field of research addressing problems that feature strongly in a wealth of scientific and industrial contexts - has been identified as one of the core potential fields of applicability of quantum computers. It is still unclear, however, to what extent quantum algorithms can actually outperform classical algorithms for this type of problems. In this work, by resorting to computational learning theory and cryptographic notions, we prove that quantum computers feature an in-principle super-polynomial advantage over classical computers in approximating solutions to combinatorial optimization problems. Specifically, building on seminal work by Kearns and Valiant and introducing a new reduction, we identify special types of problems that are hard for classical computers to approximate up to polynomial factors. At the same time, we give a quantum algorithm that can efficiently approximate the optimal solution within a polynomial factor. The core of the quantum advantage discovered in this work is ultimately borrowed from Shor's quantum algorithm for factoring. Concretely, we prove a super-polynomial advantage for approximating special instances of the so-called integer programming problem. In doing so, we provide an explicit end-to-end construction for advantage bearing instances. This result shows that quantum devices have, in principle, the power to approximate combinatorial optimization solutions beyond the reach of classical efficient algorithms. Our results also give clear guidance on how to construct such advantage-bearing problem instances.

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arXiv2024

Whispering Pixels: Exploiting Uninitialized Register Accesses in Modern GPUs

Graphic Processing Units (GPUs) have transcended their traditional use-case of rendering graphics and nowadays also serve as a powerful platform for accelerating ubiquitous, non-graphical rendering tasks. One prominent task is inference of neural networks, which process vast amounts of personal data, such as audio, text or images. Thus, GPUs became integral components for handling vast amounts of potentially confidential data, which has awakened the interest of security researchers. This lead to the discovery of various vulnerabilities in GPUs in recent years. In this paper, we uncover yet another vulnerability class in GPUs: We found that some GPU implementations lack proper register initialization routines before shader execution, leading to unintended register content leakage of previously executed shader kernels. We showcase the existence of the aforementioned vulnerability on products of 3 major vendors - Apple, NVIDIA and Qualcomm. The vulnerability poses unique challenges to an adversary due to opaque scheduling and register remapping algorithms present in the GPU firmware, complicating the reconstruction of leaked data. In order to illustrate the real-world impact of this flaw, we showcase how these challenges can be solved for attacking various workloads on the GPU. First, we showcase how uninitialized registers leak arbitrary pixel data processed by fragment shaders. We further implement information leakage attacks on intermediate data of Convolutional Neural Networks (CNNs) and present the attack's capability to leak and reconstruct the output of Large Language Models (LLMs).

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arXiv2023

Modulation to the Rescue: Identifying Sub-Circuitry in the Transistor Morass for Targeted Analysis

Physical attacks form one of the most severe threats against secure computing platforms. Their criticality arises from their corresponding threat model: By, e.g., passively measuring an integrated circuit's (IC's) environment during a security-related operation, internal secrets may be disclosed. Furthermore, by actively disturbing the physical runtime environment of an IC, an adversary can cause a specific, exploitable misbehavior. The set of physical attacks consists of techniques that apply either globally or locally. When compared to global techniques, local techniques exhibit a much higher precision, hence having the potential to be used in advanced attack scenarios. However, using physical techniques with additional spatial dependency expands the parameter search space exponentially. In this work, we present and compare two techniques, namely laser logic state imaging (LLSI) and lock-in thermography (LIT), that can be used to discover sub-circuitry of an entirely unknown IC based on optical and thermal principles. We show that the time required to identify specific regions can be drastically reduced, thus lowering the complexity of physical attacks requiring positional information. Our case study on an Intel H610 Platform Controller Hub showcases that, depending on the targeted voltage rail, our technique reduces the search space by around 90 to 98 percent.

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arXiv2023

faulTPM: Exposing AMD fTPMs' Deepest Secrets

Trusted Platform Modules constitute an integral building block of modern security features. Moreover, as Windows 11 made a TPM 2.0 mandatory, they are subject to an ever-increasing academic challenge. While discrete TPMs - as found in higher-end systems - have been susceptible to attacks on their exposed communication interface, more common firmware TPMs (fTPMs) are immune to this attack vector as they do not communicate with the CPU via an exposed bus. In this paper, we analyze a new class of attacks against fTPMs: Attacking their Trusted Execution Environment can lead to a full TPM state compromise. We experimentally verify this attack by compromising the AMD Secure Processor, which constitutes the TEE for AMD's fTPMs. In contrast to previous dTPM sniffing attacks, this vulnerability exposes the complete internal TPM state of the fTPM. It allows us to extract any cryptographic material stored or sealed by the fTPM regardless of authentication mechanisms such as Platform Configuration Register validation or passphrases with anti-hammering protection. First, we demonstrate the impact of our findings by - to the best of our knowledge - enabling the first attack against Full Disk Encryption solutions backed by an fTPM. Furthermore, we lay out how any application relying solely on the security properties of the TPM - like Bitlocker's TPM- only protector - can be defeated by an attacker with 2-3 hours of physical access to the target device. Lastly, we analyze the impact of our attack on FDE solutions protected by a TPM and PIN strategy. While a naive implementation also leaves the disk completely unprotected, we find that BitLocker's FDE implementation withholds some protection depending on the complexity of the used PIN. Our results show that when an fTPM's internal state is compromised, a TPM and PIN strategy for FDE is less secure than TPM-less protection with a reasonable passphrase.

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arXiv2023

Trojan Awakener: Detecting Dormant Malicious Hardware Using Laser Logic State Imaging (Extended Version)

The threat of hardware Trojans (HTs) and their detection is a widely studied field. While the effort for inserting a Trojan into an application-specific integrated circuit (ASIC) can be considered relatively high, especially when trusting the chip manufacturer, programmable hardware is vulnerable to Trojan insertion even after the product has been shipped or during usage. At the same time, detecting dormant HTs with small or zero-overhead triggers and payloads on these platforms is still a challenging task, as the Trojan might not get activated during the chip verification using logical testing or physical measurements. In this work, we present a novel Trojan detection approach based on a technique known from integrated circuit (IC) failure analysis, capable of detecting virtually all classes of dormant Trojans. Using laser logic state imaging (LLSI), we show how supply voltage modulations can awaken inactive Trojans, making them detectable using laser voltage imaging techniques. Therefore, our technique does not require triggering the Trojan. To support our claims, we present three case studies on 28 and 20 SRAM- and flash-based field-programmable gate arrays (FPGAs). We demonstrate how to detect with high confidence small changes in sequential and combinatorial logic as well as in the routing configuration of FPGAs in a non-invasive manner. Finally, we discuss the practical applicability of our approach on dormant analog Trojans in ASICs.

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arXiv2022

A super-polynomial quantum-classical separation for density modelling

Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, we show that there exists a density modelling problem for which fault-tolerant quantum computers can offer a super-polynomial advantage over classical learning algorithms, given standard cryptographic assumptions. Along the way, we provide a variety of additional results and insights, of potential interest for proving future distribution learning separations between quantum and classical learning algorithms. Specifically, we (a) provide an overview of the relationships between hardness results in supervised learning and distribution learning, and (b) show that any weak pseudo-random function can be used to construct a classically hard density modelling problem. The latter result opens up the possibility of proving quantum-classical separations for density modelling based on weaker assumptions than those necessary for pseudo-random functions.

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arXiv2022

EM-Fault It Yourself: Building a Replicable EMFI Setup for Desktop and Server Hardware

EMFI has become a popular fault injection (FI) technique due to its ability to inject faults precisely considering timing and location. Recently, ARM, RISC-V, and even x86 processing units in different packages were shown to be vulnerable to electromagnetic fault injection (EMFI) attacks. However, past publications lack a detailed description of the entire attack setup, hindering researchers and companies from easily replicating the presented attacks on their devices. In this work, we first show how to build an automated EMFI setup with high scanning resolution and good repeatability that is large enough to attack modern desktop and server CPUs. We structurally lay out all details on mechanics, hardware, and software along with this paper. Second, we use our setup to attack a deeply embedded security co-processor in modern AMD systems on a chip (SoCs), the AMD Secure Processor (AMD-SP). Using a previously published code execution exploit, we run two custom payloads on the AMD-SP that utilize the SoC to different degrees. We then visualize these fault locations on SoC photographs allowing us to reason about the SoC's components under attack. Finally, we show that the signature verification process of one of the first executed firmware parts is susceptible to EMFI attacks, undermining the security architecture of the entire SoC. To the best of our knowledge, this is the first reported EMFI attack against an AMD desktop CPU.

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arXiv2022

A single $T$-gate makes distribution learning hard

The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive characterization of the learnability of the output distributions of local quantum circuits. Our first result yields insight into the relationship between the efficient learnability and the efficient simulatability of these distributions. Specifically, we prove that the density modelling problem associated with Clifford circuits can be efficiently solved, while for depth $d=n^{Ω(1)}$ circuits the injection of a single $T$-gate into the circuit renders this problem hard. This result shows that efficient simulatability does not imply efficient learnability. Our second set of results provides insight into the potential and limitations of quantum generative modelling algorithms. We first show that the generative modelling problem associated with depth $d=n^{Ω(1)}$ local quantum circuits is hard for any learning algorithm, classical or quantum. As a consequence, one cannot use a quantum algorithm to gain a practical advantage for this task. We then show that, for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=ω(\log(n))$ Clifford circuits is hard. This result places limitations on the applicability of near-term hybrid quantum-classical generative modelling algorithms.

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arXiv2022

Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS

Due to the advancing development of quantum computers, practical attacks on conventional public-key cryptography may become feasible in the next few decades. To address this risk, post-quantum schemes that are secure against quantum attacks are being developed. Lattice-based algorithms are promising replacements for conventional schemes, with BLISS being one of the earliest post-quantum signature schemes in this family. However, required subroutines such as Gaussian sampling have been demonstrated to be a risk for the security of BLISS, since implementing Gaussian sampling both efficient and secure with respect to physical attacks is highly challenging. This paper presents three related power side-channel attacks on GALACTICS, the latest constant-time implementation of BLISS. All attacks are based on leakages we identified in the Gaussian sampling and signing algorithm of GALACTICS. To run the attack, a profiling phase on a device identical to the device under attack is required to train machine learning classifiers. In the attack phase, the leakages of GALACTICS enable the trained classifiers to predict sensitive internal information with high accuracy, paving the road for three different key recovery attacks. We demonstrate the leakages by running GALACTICS on a Cortex-M4 and provide proof-of-concept data and implementation for all our attacks.

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arXiv2022

Learning Classical Readout Quantum PUFs based on single-qubit gates

Physical Unclonable Functions (PUFs) have been proposed as a way to identify and authenticate electronic devices. Recently, several ideas have been presented that aim to achieve the same for quantum devices. Some of these constructions apply single-qubit gates in order to provide a secure fingerprint of the quantum device. In this work, we formalize the class of Classical Readout Quantum PUFs (CR-QPUFs) using the statistical query (SQ) model and explicitly show insufficient security for CR-QPUFs based on single qubit rotation gates, when the adversary has SQ access to the CR-QPUF. We demonstrate how a malicious party can learn the CR-QPUF characteristics and forge the signature of a quantum device through a modelling attack using a simple regression of low-degree polynomials. The proposed modelling attack was successfully implemented in a real-world scenario on real IBM Q quantum machines. We thoroughly discuss the prospects and problems of CR-QPUFs where quantum device imperfections are used as a secure fingerprint.

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arXiv2021

Learnability of the output distributions of local quantum circuits

There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two different oracle models, the probably approximately correct (PAC) learnability of quantum circuit Born machines, i.e., the output distributions of local quantum circuits. We first show a negative result, namely, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable in the statistical query model, i.e., when given query access to empirical expectation values of bounded functions over the sample space. This immediately implies the hardness, for both quantum and classical algorithms, of learning from statistical queries the output distributions of local quantum circuits using any gate set which includes the Clifford group. As many practical generative modelling algorithms use statistical queries -- including those for training quantum circuit Born machines -- our result is broadly applicable and strongly limits the possibility of a meaningful quantum advantage for learning the output distributions of local quantum circuits. As a positive result, we show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable by a classical learner. Our results are equally applicable to the problems of learning an algorithm for generating samples from the target distribution (generative modelling) and learning an algorithm for evaluating its probabilities (density modelling). They provide the first rigorous insights into the learnability of output distributions of local quantum circuits from the probabilistic modelling perspective.

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arXiv2021

VIA: Analyzing Device Interfaces of Protected Virtual Machines

Both AMD and Intel have presented technologies for confidential computing in cloud environments. The proposed solutions - AMD SEV (-ES, -SNP) and Intel TDX - protect Virtual Machines (VMs) against attacks from higher privileged layers through memory encryption and integrity protection. This model of computation draws a new trust boundary between virtual devices and the VM, which in so far lacks thorough examination. In this paper, we therefore present an analysis of the virtual device interface and discuss several attack vectors against a protected VM. Further, we develop and evaluate VIA, an automated analysis tool to detect cases of improper sanitization of input recieved via the virtual device interface. VIA improves upon existing approaches for the automated analysis of device interfaces in the following aspects: (i) support for virtualization relevant buses, (ii) efficient Direct Memory Access (DMA) support and (iii) performance. VIA builds upon the Linux Kernel Library and clang's libfuzzer to fuzz the communication between the driver and the device via MMIO, PIO, and DMA. An evaluation of VIA shows that it performs 570 executions per second on average and improves performance compared to existing approaches by an average factor of 2706. Using VIA, we analyzed 22 drivers in Linux 5.10.0-rc6, thereby uncovering 50 bugs and initiating multiple patches to the virtual device driver interface of Linux. To prove our findings criticality under the threat model of AMD SEV and Intel TDX, we showcase three exemplary attacks based on the bugs found. The attacks enable a malicious hypervisor to corrupt the memory and gain code execution in protected VMs with SEV-ES and are theoretically applicable to SEV-SNP and TDX.

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arXiv2021

One Glitch to Rule Them All: Fault Injection Attacks Against AMD's Secure Encrypted Virtualization

AMD Secure Encrypted Virtualization (SEV) offers protection mechanisms for virtual machines in untrusted environments through memory and register encryption. To separate security-sensitive operations from software executing on the main x86 cores, SEV leverages the AMD Secure Processor (AMD-SP). This paper introduces a new approach to attack SEV-protected virtual machines (VMs) by targeting the AMD-SP. We present a voltage glitching attack that allows an attacker to execute custom payloads on the AMD-SPs of all microarchitectures that support SEV currently on the market (Zen 1, Zen 2, and Zen 3). The presented methods allow us to deploy a custom SEV firmware on the AMD-SP, which enables an adversary to decrypt a VM's memory. Furthermore, using our approach, we can extract endorsement keys of SEV-enabled CPUs, which allows us to fake attestation reports or to pose as a valid target for VM migration without requiring physical access to the target host. Moreover, we reverse-engineered the Versioned Chip Endorsement Key (VCEK) mechanism introduced with SEV Secure Nested Paging (SEV-SNP). The VCEK binds the endorsement keys to the firmware version of TCB components relevant for SEV. Building on the ability to extract the endorsement keys, we show how to derive valid VCEKs for arbitrary firmware versions. With our findings, we prove that SEV cannot adequately protect confidential data in cloud environments from insider attackers, such as rogue administrators, on currently available CPUs.

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arXiv2021

The Forgotten Threat of Voltage Glitching: A Case Study on Nvidia Tegra X2 SoCs

Voltage fault injection (FI) is a well-known attack technique that can be used to force faulty behavior in processors during their operation. Glitching the supply voltage can cause data value corruption, skip security checks, or enable protected code paths. At the same time, modern systems on a chip (SoCs) are used in security-critical applications, such as self-driving cars and autonomous machines. Since these embedded devices are often physically accessible by attackers, vendors must consider device tampering in their threat models. However, while the threat of voltage FI is known since the early 2000s, it seems as if vendors still forget to integrate countermeasures. This work shows how the entire boot security of an Nvidia SoC, used in Tesla's autopilot and Mercedes-Benz's infotainment system, can be circumvented using voltage FI. We uncover a hidden bootloader that is only available to the manufacturer for testing purposes and disabled by fuses in shipped products. We demonstrate how to re-enable this bootloader using FI to gain code execution with the highest privileges, enabling us to extract the bootloader's firmware and decryption keys used in later boot stages. Using a hardware implant, an adversary might misuse the hidden bootloader to bypass trusted code execution even during the system's regular operation.

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arXiv2021

On the Quantum versus Classical Learnability of Discrete Distributions

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consider the following task: Given samples from some unknown discrete probability distribution, output with high probability an efficient algorithm for generating new samples from a good approximation of the original distribution. Our primary result is the explicit construction of a class of discrete probability distributions which, under the decisional Diffie-Hellman assumption, is provably not efficiently PAC learnable by a classical generative modelling algorithm, but for which we construct an efficient quantum learner. This class of distributions therefore provides a concrete example of a generative modelling problem for which quantum learners exhibit a provable advantage over classical learning algorithms. In addition, we discuss techniques for proving classical generative modelling hardness results, as well as the relationship between the PAC learnability of Boolean functions and the PAC learnability of discrete probability distributions.

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