Visible to the public Biblio

Filters: Keyword is side channels  [Clear All Filters]
Simon, Laurent, Chisnall, David, Anderson, Ross.  2018.  What You Get is What You C: Controlling Side Effects in Mainstream C Compilers. 2018 IEEE European Symposium on Security and Privacy (EuroS P). :1–15.
Security engineers have been fighting with C compilers for years. A careful programmer would test for null pointer dereferencing or division by zero; but the compiler would fail to understand, and optimize the test away. Modern compilers now have dedicated options to mitigate this. But when a programmer tries to control side effects of code, such as to make a cryptographic algorithm execute in constant time, the problem remains. Programmers devise complex tricks to obscure their intentions, but compiler writers find ever smarter ways to optimize code. A compiler upgrade can suddenly and without warning open a timing channel in previously secure code. This arms race is pointless and has to stop. We argue that we must stop fighting the compiler, and instead make it our ally. As a starting point, we analyze the ways in which compiler optimization breaks implicit properties of crypto code; and add guarantees for two of these properties in Clang/LLVM. Our work explores what is actually involved in controlling side effects on modern CPUs with a standard toolchain. Similar techniques can and should be applied to other security properties; achieving intentions by compiler commands or annotations makes them explicit, so we can reason about them. It is already understood that explicitness is essential for cryptographic protocol security and for compiler performance; it is essential for language security too. We therefore argue that this should be only the first step in a sustained engineering effort.
Tiwari, Trishita, Trachtenberg, Ari.  2018.  Cashing in on the File-System Cache. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :2303-2305.

We consider the disk cache (file-system cache) information channel, and show how it can be exploited on various systems to yield potentially sensitive information. Our approach can be used locally by an unprivileged adversary to detect whether another user is writing to disk, and if so, the rate at which data is being written. Further, we also show how an attacker can detect whether specific files have been recently accessed by the victim. We then extend this attack to remote access through a web server, using timing analysis to identify recent access of chosen pages.

Belikovetsky, S., Solewicz, Y., Yampolskiy, M., Toh, J., Elovici, Y..  2018.  Digital Audio Signature for 3D Printing Integrity. IEEE Transactions on Information Forensics and Security. :1–1.

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer’s stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes and different audio recorder positions.

Chen, Jia, Feng, Yu, Dillig, Isil.  2017.  Precise Detection of Side-Channel Vulnerabilities Using Quantitative Cartesian Hoare Logic. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :875–890.
This paper presents Themis, an end-to-end static analysis tool for finding resource-usage side-channel vulnerabilities in Java applications. We introduce the notion of epsilon-bounded non-interference, a variant and relaxation of Goguen and Meseguer's well-known non-interference principle. We then present Quantitative Cartesian Hoare Logic (QCHL), a program logic for verifying epsilon-bounded non-interference. Our tool, Themis, combines automated reasoning in CHL with lightweight static taint analysis to improve scalability. We evaluate Themis on well known Java applications and demonstrate that Themis can find unknown side-channel vulnerabilities in widely-used programs. We also show that Themis can verify the absence of vulnerabilities in repaired versions of vulnerable programs and that Themis compares favorably against Blazer, a state-of-the-art static analysis tool for finding timing side channels in Java applications.
Paul-Pena, D., Krishnamurthy, P., Karri, R., Khorrami, F..  2017.  Process-aware side channel monitoring for embedded control system security. 2017 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC). :1–6.

Cyber-physical systems (CPS) are interconnections of heterogeneous hardware and software components (e.g., sensors, actuators, physical systems/processes, computational nodes and controllers, and communication subsystems). Increasing network connectivity of CPS computational nodes facilitates maintenance and on-demand reprogrammability and reduces operator workload. However, such increasing connectivity also raises the potential for cyber-attacks that attempt unauthorized modifications of run-time parameters or control logic in the computational nodes to hamper process stability or performance. In this paper, we analyze the effectiveness of real-time monitoring using digital and analog side channels. While analog side channels might not typically provide sufficient granularity to observe each iteration of a periodic loop in the code in the CPS device, the temporal averaging inherent to side channel sensory modalities enables observation of persistent changes to the contents of a computational loop through their resulting effect on the level of activity of the device. Changes to code can be detected by observing readings from side channel sensors over a period of time. Experimental studies are performed on an ARM-based single board computer.

Hojjati, Avesta, Adhikari, Anku, Struckmann, Katarina, Chou, Edward, Tho Nguyen, Thi Ngoc, Madan, Kushagra, Winslett, Marianne S., Gunter, Carl A., King, William P..  2016.  Leave Your Phone at the Door: Side Channels That Reveal Factory Floor Secrets. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :883–894.

From pencils to commercial aircraft, every man-made object must be designed and manufactured. When it is cheaper or easier to steal a design or a manufacturing process specification than to invent one's own, the incentive for theft is present. As more and more manufacturing data comes online, incidents of such theft are increasing. In this paper, we present a side-channel attack on manufacturing equipment that reveals both the form of a product and its manufacturing process, i.e., exactly how it is made. In the attack, a human deliberately or accidentally places an attack-enabled phone close to the equipment or makes or receives a phone call on any phone nearby. The phone executing the attack records audio and, optionally, magnetometer data. We present a method of reconstructing the product's form and manufacturing process from the captured data, based on machine learning, signal processing, and human assistance. We demonstrate the attack on a 3D printer and a CNC mill, each with its own acoustic signature, and discuss the commonalities in the sensor data captured for these two different machines. We compare the quality of the data captured with a variety of smartphone models. Capturing data from the 3D printer, we reproduce the form and process information of objects previously unknown to the reconstructors. On average, our accuracy is within 1 mm in reconstructing the length of a line segment in a fabricated object's shape and within 1 degree in determining an angle in a fabricated object's shape. We conclude with recommendations for defending against these attacks.