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Adversarial Machine Learning and the CFAA

I just co-authored a paper on the legal risks of doing machine learning research, given the current state of the Computer Fraud and Abuse Act:

Abstract: Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, “What are the potential legal risks to adversarial ML researchers when they attack ML systems?” Studying or testing the security of any operational system potentially runs afoul the Computer Fraud and Abuse Act (CFAA), the primary United States federal statute that creates liability for hacking. We claim that Adversarial ML research is likely no different. Our analysis show that because there is a split in how CFAA is interpreted, aspects of adversarial ML attacks, such as model inversion, membership inference, model stealing, reprogramming the ML system and poisoning attacks, may be sanctioned in some jurisdictions and not penalized in others. We conclude with an analysis predicting how the US Supreme Court may resolve some present inconsistencies in the CFAA’s application in Van Buren v. United States, an appeal expected to be decided in 2021. We argue that the court is likely to adopt a narrow construction of the CFAA, and that this will actually lead to better adversarial ML security outcomes in the long term.

Medium post on the paper. News article, which uses our graphic without attribution.

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Fawkes: Digital Image Cloaking

Fawkes is a system for manipulating digital images so that they aren’t recognized by facial recognition systems.

At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these “cloaked” photos as you normally would, sharing them on social media, sending them to friends, printing them or displaying them on digital devices, the same way you would any other photo. The difference, however, is that if and when someone tries to use these photos to build a facial recognition model, “cloaked” images will teach the model an highly distorted version of what makes you look like you. The cloak effect is not easily detectable, and will not cause errors in model training. However, when someone tries to identify you using an unaltered image of you (e.g. a photo taken in public), and tries to identify you, they will fail.

Research paper.

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Hacking a Power Supply

This hack targets the firmware on modern power supplies. (Yes, power supplies are also computers.)

Normally, when a phone is connected to a power brick with support for fast charging, the phone and the power adapter communicate with each other to determine the proper amount of electricity that can be sent to the phone without damaging the device­ — the more juice the power adapter can send, the faster it can charge the phone.

However, by hacking the fast charging firmware built into a power adapter, Xuanwu Labs demonstrated that bad actors could potentially manipulate the power brick into sending more electricity than a phone can handle, thereby overheating the phone, melting internal components, or as Xuanwu Labs discovered, setting the device on fire.

Research paper, in Chinese.

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Securing the International IoT Supply Chain

Together with Nate Kim (former student) and Trey Herr (Atlantic Council Cyber Statecraft Initiative), I have written a paper on IoT supply chain security. The basic problem we try to solve is: how to you enforce IoT security regulations when most of the stuff is made in other countries? And our solution is: enforce the regulations on the domestic company that’s selling the stuff to consumers. There’s a lot of detail between here and there, though, and it’s all in the paper.

We also wrote a Lawfare post:

…we propose to leverage these supply chains as part of the solution. Selling to U.S. consumers generally requires that IoT manufacturers sell through a U.S. subsidiary or, more commonly, a domestic distributor like Best Buy or Amazon. The Federal Trade Commission can apply regulatory pressure to this distributor to sell only products that meet the requirements of a security framework developed by U.S. cybersecurity agencies. That would put pressure on manufacturers to make sure their products are compliant with the standards set out in this security framework, including pressuring their component vendors and original device manufacturers to make sure they supply parts that meet the recognized security framework.

News article.

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The Unintended Harms of Cybersecurity

Interesting research: “Identifying Unintended Harms of Cybersecurity Countermeasures“:

Abstract: Well-meaning cybersecurity risk owners will deploy countermeasures (technologies or procedures) to manage risks to their services or systems. In some cases, those countermeasures will produce unintended consequences, which must then be addressed. Unintended consequences can potentially induce harm, adversely affecting user behaviour, user inclusion, or the infrastructure itself (including other services or countermeasures). Here we propose a framework for preemptively identifying unintended harms of risk countermeasures in cybersecurity.The framework identifies a series of unintended harms which go beyond technology alone, to consider the cyberphysical and sociotechnical space: displacement, insecure norms, additional costs, misuse, misclassification, amplification, and disruption. We demonstrate our framework through application to the complex,multi-stakeholder challenges associated with the prevention of cyberbullying as an applied example. Our framework aims to illuminate harmful consequences, not to paralyze decision-making, but so that potential unintended harms can be more thoroughly considered in risk management strategies. The framework can support identification and preemptive planning to identify vulnerable populations and preemptively insulate them from harm. There are opportunities to use the framework in coordinating risk management strategy across stakeholders in complex cyberphysical environments.

Security is always a trade-off. I appreciate work that examines the details of that trade-off.

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Analyzing IoT Security Best Practices

New research: “Best Practices for IoT Security: What Does That Even Mean?” by Christopher Bellman and Paul C. van Oorschot:

Abstract: Best practices for Internet of Things (IoT) security have recently attracted considerable attention worldwide from industry and governments, while academic research has highlighted the failure of many IoT product manufacturers to follow accepted practices. We explore not the failure to follow best practices, but rather a surprising lack of understanding, and void in the literature, on what (generically) “best practice” means, independent of meaningfully identifying specific individual practices. Confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. How do best practices, good practices, and standard practices differ? Or guidelines, recommendations, and requirements? Can something be a best practice if it is not actionable? We consider categories of best practices, and how they apply over the lifecycle of IoT devices. For concreteness in our discussion, we analyze and categorize a set of 1014 IoT security best practices, recommendations, and guidelines from industrial, government, and academic sources. As one example result, we find that about 70% of these practices or guidelines relate to early IoT device lifecycle stages, highlighting the critical position of manufacturers in addressing the security issues in question. We hope that our work provides a basis for the community to build on in order to better understand best practices, identify and reach consensus on specific practices, and then find ways to motivate relevant stakeholders to follow them.

Back in 2017, I catalogued nineteen security and privacy guideline documents for the Internet of Things. Our problem right now isn’t that we don’t know how to secure these devices, it’s that there is no economic or regulatory incentive to do so.

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Cryptocurrency Pump and Dump Scams

Really interesting research: “An examination of the cryptocurrency pump and dump ecosystem“:

Abstract: The surge of interest in cryptocurrencies has been accompanied by a proliferation of fraud. This paper examines pump and dump schemes. The recent explosion of nearly 2,000 cryptocurrencies in an unregulated environment has expanded the scope for abuse. We quantify the scope of cryptocurrency pump and dump schemes on Discord and Telegram, two popular group-messaging platforms. We joined all relevant Telegram and Discord groups/channels and identified thousands of different pumps. Our findings provide the first measure of the scope of such pumps and empirically document important properties of this ecosystem.

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Eavesdropping on Sound Using Variations in Light Bulbs

New research is able to recover sound waves in a room by observing minute changes in the room’s light bulbs. This technique works from a distance, even from a building across the street through a window.

Details:

In an experiment using three different telescopes with different lens diameters from a distance of 25 meters (a little over 82 feet) the researchers were successfully able to capture sound being played in a remote room, including The Beatles’ Let It Be, which was distinguishable enough for Shazam to recognize it, and a speech from President Trump that Google’s speech recognition API could successfully transcribe. With more powerful telescopes and a more sensitive analog-to-digital converter, the researchers believe the eavesdropping distances could be even greater.

It’s not expensive: less than $1,000 worth of equipment is required. And unlike other techniques like bouncing a laser off the window and measuring the vibrations, it’s completely passive.

News articles.

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Availability Attacks against Neural Networks

New research on using specially crafted inputs to slow down machine-learning neural network systems:

Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these examples for all sorts of mischief — from draining mobile phone batteries, though degrading the machine-vision systems on which self-driving cars rely, to jamming cognitive radar.

So far, our most spectacular results are against NLP systems. By feeding them confusing inputs we can slow them down over 100 times. There are already examples in the real world where people pause or stumble when asked hard questions but we now have a dependable method for generating such examples automatically and at scale. We can also neutralize the performance improvements of accelerators for computer vision tasks, and make them operate on their worst case performance.

The paper.

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Security Analysis of the Democracy Live Online Voting System

New research: “Security Analysis of the Democracy Live Online Voting System“:

Abstract: Democracy Live’s OmniBallot platform is a web-based system for blank ballot delivery, ballot marking, and (optionally) online voting. Three states — Delaware, West Virginia, and New Jersey — recently announced that they will allow certain voters to cast votes online using OmniBallot, but, despite the well established risks of Internet voting, the system has never been the subject of a public, independent security review.

We reverse engineered the client-side portion of OmniBallot, as used in Delaware, in order to detail the system’s operation and analyze its security.We find that OmniBallot uses a simplistic approach to Internet voting that is vulnerable to vote manipulation by malware on the voter’s device and by insiders or other attackers who can compromise Democracy Live, Amazon,Google, or Cloudflare. In addition, Democracy Live, which appears to have no privacy policy, receives sensitive personally identifiable information­ — including the voter’s identity, ballot selections, and browser fingerprint­ — that could be used to target political ads or disinformation campaigns.Even when OmniBallot is used to mark ballots that will be printed and returned in the mail, the software sends the voter’s identity and ballot choices to Democracy Live, an unnecessary security risk that jeopardizes the secret ballot. We recommend changes to make the platform safer for ballot delivery and marking. However, we conclude that using OmniBallot for electronic ballot return represents a severe risk to election security and could allow attackers to alter election results without detection.

News story.

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