arXiv subjects

Eran Tromer

Publications and source records attributed to Eran Tromer.

5 recordsLinked to original sources

arXiv2023

Cheesecloth: Zero-Knowledge Proofs of Real-World Vulnerabilities

Currently, when a security analyst discovers a vulnerability in critical software system, they must navigate a fraught dilemma: immediately disclosing the vulnerability to the public could harm the system's users; whereas disclosing the vulnerability only to the software's vendor lets the vendor disregard or deprioritize the security risk, to the detriment of unwittingly-affected users. A compelling recent line of work aims to resolve this by using Zero Knowledge (ZK) protocols that let analysts prove that they know a vulnerability in a program, without revealing the details of the vulnerability or the inputs that exploit it. In principle, this could be achieved by generic ZK techniques. In practice, ZK vulnerability proofs to date have been restricted in scope and expressibility, due to challenges related to generating proof statements that model real-world software at scale and to directly formulating violated properties. This paper presents CHEESECLOTH, a novel proofstatement compiler, which proves practical vulnerabilities in ZK by soundly-but-aggressively preprocessing programs on public inputs, selectively revealing information about executed control segments, and formalizing information leakage using a novel storage-labeling scheme. CHEESECLOTH's practicality is demonstrated by generating ZK proofs of well-known vulnerabilities in (previous versions of) critical software, including the Heartbleed information leakage in OpenSSL and a memory vulnerability in the FFmpeg graphics framework.

cs.CR↗

arXiv2022

Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity

A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amendable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much "novelty" is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. "Novelty" is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.

cs.CY↗

arXiv2020

You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion

Code autocompletion is an integral feature of modern code editors and IDEs. The latest generation of autocompleters uses neural language models, trained on public open-source code repositories, to suggest likely (not just statically feasible) completions given the current context. We demonstrate that neural code autocompleters are vulnerable to poisoning attacks. By adding a few specially-crafted files to the autocompleter's training corpus (data poisoning), or else by directly fine-tuning the autocompleter on these files (model poisoning), the attacker can influence its suggestions for attacker-chosen contexts. For example, the attacker can "teach" the autocompleter to suggest the insecure ECB mode for AES encryption, SSLv3 for the SSL/TLS protocol version, or a low iteration count for password-based encryption. Moreover, we show that these attacks can be targeted: an autocompleter poisoned by a targeted attack is much more likely to suggest the insecure completion for files from a specific repo or specific developer. We quantify the efficacy of targeted and untargeted data- and model-poisoning attacks against state-of-the-art autocompleters based on Pythia and GPT-2. We then evaluate existing defenses against poisoning attacks and show that they are largely ineffective.

cs.CR↗

arXiv2019

Synesthesia: Detecting Screen Content via Remote Acoustic Side Channels

We show that subtle acoustic noises emanating from within computer screens can be used to detect the content displayed on the screens. This sound can be picked up by ordinary microphones built into webcams or screens, and is inadvertently transmitted to other parties, e.g., during a videoconference call or archived recordings. It can also be recorded by a smartphone or "smart speaker" placed on a desk next to the screen, or from as far as 10 meters away using a parabolic microphone. Empirically demonstrating various attack scenarios, we show how this channel can be used for real-time detection of on-screen text, or users' input into on-screen virtual keyboards. We also demonstrate how an attacker can analyze the audio received during video call (e.g., on Google Hangout) to infer whether the other side is browsing the web in lieu of watching the video call, and which web site is displayed on their screen.

cs.CR↗

arXiv2011

Using More Data to Speed-up Training Time

In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the number of examples, and spell-out several interesting open problems.

cs.LG↗