Daily Archives: March 22, 2024

Apple accused of monopolizing smartphone markets by US and 15 states. Loses $115 billion market cap

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The U.S. Department of Justice and 15 states on Thursday sued Apple (AAPL.O)

, opens new tab as the government cracks down on Big Tech, alleging the iPhone maker monopolized the smartphone market, hurt smaller rivals and drove up prices.
Apple joins competitors sued by regulators, including Alphabet’s (GOOGL.O)

, opens new tab Google, Meta Platforms (META.O), opens new tab and Amazon.com (AMZN.O)

, opens new tab across the administrations of both former President Donald Trump and President Joe Biden.
“Consumers should not have to pay higher prices because companies violate the antitrust laws,” Attorney General Merrick Garland said in a statement. “If left unchallenged, Apple will only continue to strengthen its smartphone monopoly.”
The Justice Department said that Apple charges as much as $1,599 for an iPhone and makes larger profit than any others in the industry. Officials also said Apple charges various business partners – from software developers to credit card companies and even its rivals such as Google – behind the scenes in ways that ultimately raise prices for consumers and drive up Apple’s profit.
Dating back to its time as a marginal player in the personal computer market, Apple’s business model has long been based on charging users a premium for technology products where the company dictates nearly all of the details of how the device works and can be used. The Justice Department seeks to unwind that business model by forcing Apple, which has a market value of $2.7 trillion, to offer users more choices around how apps can tap in to the hardware that Apple designs.
[…]
The Justice Department, which was also joined by the District of Columbia in the lawsuit, is seeking changes at Apple. An official suggested some form of breakup or reduction of the size of Apple was a possibility when they noted “structural relief is also a form of equitable relief.”
The 88-page lawsuit, filed in U.S. federal court in Newark, New Jersey, said it was focused on “freeing smartphone markets from Apple’s anticompetitive and exclusionary conduct and restoring competition to lower smartphone prices for consumers, reducing fees for developers, and preserving innovation for the future.”
In the lawsuit, the U.S. accused Apple of making it harder for consumers to block competitors and cited five examples where Apple used mechanisms to suppress technologies that would have increased competition among smartphones: so-called super apps, cloud stream game apps, messaging apps, smartwatches and digital wallets.
For example, the U.S. alleges Apple made it more difficult for competing messaging apps and smartwatches to work smoothly on its phones. It also alleges that Apple’s app store policies around streaming services for games have hurt competition.
[…]
On Thursday Reuters reported that Apple, Meta Platforms and Alphabet’s Google will be investigated for potential violations of the European Union’s Digital Markets Act that could lead to hefty fines by the end of the year, according to people with direct knowledge of the matter.
In Europe, Apple’s App Store business model has been dismantled by a new law called the Digital Markets Act that went into effect earlier this month. Apple plans to let developers offer their own app stores – and, importantly, pay no commissions – but rivals such as Spotify (SPOT.N)

, opens new tab and Epic argue Apple is still making it too hard to offer alternative app stores.

Source: Apple accused of monopolizing smartphone markets in US antitrust lawsuit | Reuters

Also: Apple Loses $113 Billion in Value After Regulators Close In | Bloomberg

Burglars using Wifi jammers and deauth attacks to disable wireless smart home security

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Edina police believe that the suspects aren’t choosing houses at random –they’re researching carefully prior to burglarizing them. The suspects are stealing jewelry, safes, and high-end merchandise.

“It’s believed the burglars are not violent and tend to choose unoccupied houses,” the police’s report reads.

At the city safety meeting on January 31st, residents warned about the burglars using WiFi jammers to impact security systems, especially surveillance cameras.

Many home security devices connect directly to the WiFi network or a smart home hub using radio frequencies such as 2.4 GHz. Their signal strength is limited and is susceptible to interference.

Jammers can overpower signals from security devices by sending a “loud” noise in the same range of frequencies. For receivers, it’s then impossible to distinguish between the genuine signals and the disruptive noise generated by the jammers.

The use of jammers in the United States is banned by the Federal Communications Commission

Source: Burglars using jammers to disable wireless smart home security | Cybernews

De-authing involves sending packets which disconnect devices from the network and is much easier than jamming.

Researchers jailbreak AI chatbots with ASCII art

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Researchers based in Washington and Chicago have developed ArtPrompt, a new way to circumvent the safety measures built into large language models (LLMs). According to the research paper ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs, chatbots such as GPT-3.5, GPT-4, Gemini, Claude, and Llama2 can be induced to respond to queries they are designed to reject using ASCII art prompts generated by their ArtPrompt tool. It is a simple and effective attack, and the paper provides examples of the ArtPrompt-induced chatbots advising on how to build bombs and make counterfeit money.

[…]

To best understand ArtPrompt and how it works, it is probably simplest to check out the two examples provided by the research team behind the tool. In Figure 1 above, you can see that ArtPrompt easily sidesteps the protections of contemporary LLMs. The tool replaces the ‘safety word’ with an ASCII art representation of the word to form a new prompt. The LLM recognizes the ArtPrompt prompt output but sees no issue in responding, as the prompt doesn’t trigger any ethical or safety safeguards.

(Image credit: arXiv:2402.11753)

Another example provided in the research paper shows us how to successfully query an LLM about counterfeiting cash. Tricking a chatbot this way seems so basic, but the ArtPrompt developers assert how their tool fools today’s LLMs “effectively and efficiently.” Moreover, they claim it “outperforms all [other] attacks on average” and remains a practical, viable attack for multimodal language models for now.

[…]

Source: Researchers jailbreak AI chatbots with ASCII art — ArtPrompt bypasses safety measures to unlock malicious queries | Tom’s Hardware

HackAPrompt – a taxonomy of GPT prompt hacking techniques

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[…] We present a comprehensive Taxonomical Ontology of Prompt Hacking techniques, which categorizes various methods used to manipulate Large Language Models (LLMs) through prompt hacking. This taxonomical ontology ranges from simple instructions and cognitive hacking to more complex techniques like context overflow, obfuscation, and code injection, offering a detailed insight into the diverse strategies used in prompt hacking attacks.

Taxonomical Ontology of Prompt HackingFigure 5: A Taxonomical Ontology of Prompt Hacking techniques. Blank lines are hypernyms (i.e., typos are an instance of obfuscation), while grey arrows are meronyms (i.e., Special Case attacks usually contain a Simple Instruction). Purple nodes are not attacks themselves but can be a part of attacks. Red nodes are specific examples.

Introducing the HackAPrompt Dataset

This dataset, comprising over 600,000 prompts, is split into two distinct collections: the Playground Dataset and the Submissions Dataset. The Playground Dataset provides a broad overview of the prompt hacking process through completely anonymous prompts tested on the interface, while the Submissions Dataset offers a more detailed insight with refined prompts submitted to the leaderboard, exhibiting a higher success rate of high-quality injections.

[…]

The table below contains success rates and total distribution of prompts for the two datasets.

Total Prompts Successful Prompts Success Rate
Submissions 41,596 34,641 83.2%
Playground 560,161 43,295 7.7%

Table 2: With a much higher success rate, the Submissions Dataset dataset contains a denser quantity of high quality injections. In contract, Playground Dataset is much larger and demonstrates competitor exploration of the task.

Source: HackAPrompt