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Setting the Swift Language mode for an SPM Bundle – Donny Wals


If you create a brand new Swift Bundle in Xcode 16, the Bundle.swift contents will look a bit like this:

// swift-tools-version: 6.0
// The swift-tools-version declares the minimal model of Swift required to construct this package deal.

import PackageDescription

let package deal = Bundle(
    title: "AppCore",
    merchandise: [
        // Products define the executables and libraries a package produces, making them visible to other packages.
        .library(
            name: "AppCore",
            targets: ["AppCore"]),
    ],
    targets: [
        // Targets are the basic building blocks of a package, defining a module or a test suite.
        // Targets can depend on other targets in this package and products from dependencies.
        .target(
            name: "AppCore"
        ),

    ]
)

Discover how the package deal’s Swift instruments model is ready to six.0. If you need your venture to reference iOS18 for instance, you are going to have to have you ever Swift instruments model set to six.0. A facet impact of that’s that your package deal will now construct within the Swift 6 language mode. Which means that you’ll get Swift’s full suite of sendability and concurrency checks in your package deal, and that the compiler will flag any points as errors.

You won’t be prepared to make use of Swift 6.0 in your new packages but. In these circumstances you possibly can both set the Swift instruments model again to five.10 in case you’re not utilizing any options from the 6.0 toolchain anyway or you possibly can set your package deal’s language mode to Swift 5 whereas conserving the 6.0 toolchain:

// swift-tools-version: 6.0
// The swift-tools-version declares the minimal model of Swift required to construct this package deal.

import PackageDescription

let package deal = Bundle(
    title: "AppCore",
    platforms: [.iOS(.v18)],
    // ... the remainder of the package deal description
    swiftLanguageModes: [.version("5")]
)

By utilizing the Swift 5 language mode you possibly can proceed to jot down your code as normal till you are prepared to begin migrating to Swift 6. For instance, you may need to begin by enabling strict concurrency checks.

Howdy, is it me you’re in search of? How scammers get your cellphone quantity

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Scams

Your humble cellphone quantity is extra invaluable than you might assume. Right here’s the way it may fall into the mistaken fingers – and how one can assist hold it out of the attain of fraudsters.

Hello, is it me you’re looking for? How scammers get your phone number

What is perhaps one of many best methods to rip-off somebody out of their cash – anonymously, in fact?

Would it not contain stealing their bank card information, maybe utilizing digital skimming or after hacking right into a database of delicate private info? Whereas efficient, these strategies could also be resource-intensive and require some technical prowess.

What about stealing cost data through pretend web sites? This will likely certainly match the invoice, however spoofing official web sites (and e mail addresses to “unfold the phrase”) might not be for everyone, both. The percentages are additionally excessive that such ploys can be noticed in time by the security-savvy amongst us or thwarted by safety controls.

As a substitute, dangerous actors are turning to extremely scalable operations that depend on refined social engineering techniques and prices little to function. Utilizing voice phishing (additionally known as vishing) and message scams (smishing), these operations have been developed right into a rip-off call-center trade value billions of {dollars}.

For starters, these ploys could not require a lot in the way in which of specialised or technical abilities. Additionally, a single particular person (usually a sufferer of human trafficking) can, at a time, ensnare a number of unwitting victims in numerous flavors of fraud. These usually contain pig butchering, cryptocurrency schemes, romance scams, and tech assist fraud, every of which spins a compelling yarn and preys on a few of what truly makes us human.

Figure 1. Would you take the bait
Would you are taking the bait?

Howdy? Is that this factor on?

Think about receiving a name out of your financial institution to tell you that your account has been breached and with the intention to hold your cash protected, you have to share your delicate particulars with them. The urgency within the voice of the financial institution’s “worker” could certainly be sufficient to immediate you to share your delicate info. The issue is, this particular person won’t be out of your financial institution – or they might not even exist in any respect. It might be only a fabricated voice, however nonetheless sound utterly pure.

This isn’t in any respect unusual and cautionary tales from current years abound. Again in 2019, a CEO was scammed out of virtually US$250,000 by a convincing voice deepfake of their mother or father firm’s chief. Equally, a finance employee was tricked through a deepfake video name in 2024, costing their agency US$25 million.

AI, the enabler

With trendy AI voice cloning and translation capabilities, vishing and smishing have develop into simpler than ever. Certainly, ESET World Cybersecurity Advisor Jake Moore demonstrated the benefit with which anyone can create a convincing deepfake model of another person – together with somebody properly. Seeing and listening to are now not believing.

 

 Example of initial contact. This message, which is making the rounds in Slovakia, is duly translated into Slovak (it says “Good morning”).
Instance of preliminary contact. This message, which is making the rounds in Slovakia, is duly translated into Slovak (it says “Good morning”).

AI is reducing the barrier of entry for brand spanking new adversaries, serving as a multipronged instrument to collect information, automate tedious duties, and globalize their attain. Consequently, phishing utilizing AI-generated voices and textual content will most likely develop into extra commonplace.

On this observe, a current report by Enea famous a 1,265% rise in phishing scams for the reason that launch of ChatGPT in November 2022 and spotlighted the potential of enormous language fashions to assist gas such malicious operations.

What’s your identify, what’s your quantity?

As evidenced by Shopper Stories analysis from 2022, persons are turning into extra privacy-conscious than earlier than. Some 75% of the survey’s respondents have been no less than considerably involved in regards to the privateness of their information collected on-line, which can embrace cellphone numbers, as they’re a invaluable useful resource for each identification and promoting.  

However now that we’re properly previous the age of the Yellow Pages, how does this connection between cellphone numbers and promoting work?

Contemplate this illustrative instance: a baseball fan positioned tickets in a devoted app’s checkout however did not full the acquisition. And but, shortly after closing the app, he obtained a cellphone name providing a reduction on the tickets. Naturally, he was baffled since he didn’t keep in mind offering his cellphone quantity to the app. How did it get his quantity, then?

The reply is – through monitoring. Some trackers can acquire particular info from a webpage, so after you’ve crammed of their cellphone quantity in a type, a tracker may detect and retailer it to create what is usually known as customized content material and expertise. There may be a complete enterprise mannequin often called “information brokering”, and the dangerous information is that it doesn’t take a breach for the information to develop into public.

Monitoring, information brokers, and leaks

Information brokers vacuum up your private info from publicly out there sources (authorities licenses/registrations), business sources (enterprise companions like bank card suppliers or shops) in addition to by monitoring your on-line actions (actions on social media, advert clicks, and many others.), earlier than promoting your info to others.

Nonetheless, the query in your lips could also be: how can scammers receive different individuals’s cellphone numbers?

Looking for victims via WhatsApp
In search of victims

Naturally, the extra corporations, websites, and apps you share your private info with, the extra detailed your private “advertising and marketing profile” is. This additionally will increase your publicity to information leaks, since information brokers themselves can expertise safety incidents. A knowledge dealer may additionally promote your info to others, probably together with dangerous actors.

However information brokers, or breaches affecting them, aren’t the one supply of cellphone numbers for scammers. Listed below are another methods during which criminals can get ahold of your cellphone quantity:

  • Public sources: Social media websites or on-line job markets would possibly present your cellphone quantity as a method to make a connection. In case your privateness settings should not dialed in accurately or you aren’t conscious of the implications of revealing your cellphone quantity in your social media profile, your quantity is perhaps out there to anybody, even an AI internet scraper.
  • Stolen accounts: Varied on-line providers require your cellphone quantity, be it to verify your identification, to position an order, or to function an authentication issue. When your accounts get brute-forced on account of weak passwords or considered one of your on-line suppliers suffers an information breach your quantity may simply leak as properly.
  • Autodialers: Autodialers name random numbers, and as quickly as you reply the decision, you might be focused by a rip-off. Generally these autodialers name simply to verify that the quantity is in use in order that it may be added to a listing of targets.
  • Mail: Test any of your current deliveries – these often have your handle seen on the letter/field, however in some instances, they’ll even have your e mail or cellphone quantity printed on them. What if somebody stole considered one of your deliveries or rummaged by way of your recycling pile? Contemplating that information leaks often comprise the identical info, this may be very harmful and grounds for additional exploitation.

For example of a wide-scale breach affecting cellphone numbers, AT&T lately revealed that hundreds of thousands of shoppers’ name and textual content message information from mid-to-late 2022 have been uncovered in an enormous information leak. Almost all the firm’s prospects and other people utilizing the cell community have had their numbers, name durations, and variety of name interactions uncovered. Whereas name and textual content contents are allegedly not among the many breached information, buyer names and numbers can nonetheless be simply linked, as reported by CNN.

Reportedly, the blame may be placed on a third-party cloud platform, which a malicious actor had accessed. Coincidentally, the identical platform has had a number of instances of large leaks linked to it lately.

How you can shield your cellphone quantity

So, how will you shield your self and your quantity? Listed below are a couple of suggestions:

  • Concentrate on phishing. By no means reply unsolicited messages/calls from overseas numbers, don’t click on on random hyperlinks in your emails/messages, and keep in mind to maintain cool and assume earlier than you react to a seemingly pressing state of affairs, as a result of that’s how they get you.
  • Ask your service supplier about their SIM safety measures. They could have an possibility for card locks to guard towards SIM swapping, for instance, or further account safety layers to forestall scams like name forwarding.
  • Shield your accounts with two-factor authentication, ideally utilizing devoted safety keys, apps, or biometrics as an alternative of SMS-based verification. The latter may be intercepted by dangerous actors with relative ease. Do the identical for service supplier accounts as properly.
  • Suppose twice earlier than offering your cellphone quantity to a web site. Whereas having it as a further restoration possibility in your numerous apps is perhaps helpful, different strategies like secondary emails/authenticators may supply a safer various.
  • For on-line purchases, think about using a pre-paid SIM card or a VoIP service as an alternative of your common cellphone quantity.
  • Use a cellular safety resolution with name filtering, in addition to be sure that third-party cookies in your internet browser are blocked, and discover different privacy-enhancing instruments and applied sciences.

In a world that more and more depends on on-line report preserving, there’s a low likelihood that your quantity received’t be preserved by a 3rd celebration someplace. And because the AT&T incident suggests, relying by yourself service’s safety can be slightly problematic. This doesn’t imply that you must stay in a state of fixed paranoia, although.

Alternatively, it highlights the significance of committing to correct cyber hygiene and being conscious of your information on-line. Vigilance can be nonetheless key, particularly when contemplating the implications of this new, AI-powered (beneath)world.

Leak reveals Snapdragon 8 Gen 4 might have two variations with Oryon CPUs

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What that you must know

  • A brand new datasheet leak reveals the specs of the upcoming Snapdragon 8 Gen 4 SoC.
  • The flagship chipset will doubtless sport two variants with SM8750 and SM8750P mannequin numbers.
  • The latter is anticipated to be performance-oriented in comparison with the common one.

Qualcomm might be gearing up for its subsequent flagship SoC via its Snapdragon Summit in a few months. Right here, we anticipate to see the Snapdragon 8 Gen 4, which is already confirmed to sport an all-new Oryon CPU.

After seeing a couple of benchmark leaks, a brand new datasheet (through SmartPrix) has leaked, revealing among the essential specs of the upcoming chipset. The attention-grabbing one within the lot is probably the 2 mannequin numbers of the identical chip bearing SM8750 and SM8750P. This implies we would see two variants of Qualcomm’s flagship SoC.



Ultimate Reduce Professional for iPad 2.0 and Ultimate Reduce Digital camera evaluate

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approaches to DARPA’s AI Cyber Problem


The US Protection Superior Analysis Initiatives Company, DARPA, just lately kicked off a two-year AI Cyber Problem (AIxCC), inviting prime AI and cybersecurity specialists to design new AI techniques to assist safe main open supply initiatives which our vital infrastructure depends upon. As AI continues to develop, it’s essential to speculate in AI instruments for Defenders, and this competitors will assist advance expertise to take action. 

Google’s OSS-Fuzz and Safety Engineering groups have been excited to help AIxCC organizers in designing their challenges and competitors framework. We additionally playtested the competitors by constructing a Cyber Reasoning System (CRS) tackling DARPA’s exemplar problem. 

This weblog put up will share our method to the exemplar problem utilizing open supply expertise present in Google’s OSS-Fuzz,  highlighting alternatives the place AI can supercharge the platform’s skill to search out and patch vulnerabilities, which we hope will encourage modern options from opponents.

AIxCC challenges concentrate on discovering and fixing vulnerabilities in open supply initiatives. OSS-Fuzz, our fuzz testing platform, has been discovering vulnerabilities in open supply initiatives as a public service for years, leading to over 11,000 vulnerabilities discovered and stuck throughout 1200+ initiatives. OSS-Fuzz is free, open supply, and its initiatives and infrastructure are formed very equally to AIxCC challenges. Opponents can simply reuse its current toolchains, fuzzing engines, and sanitizers on AIxCC initiatives. Our baseline Cyber Reasoning System (CRS) primarily leverages non-AI methods and has some limitations. We spotlight these as alternatives for opponents to discover how AI can advance the cutting-edge in fuzz testing.

For userspace Java and C/C++ challenges, fuzzing with engines resembling libFuzzer, AFL(++), and Jazzer is simple as a result of they use the identical interface as OSS-Fuzz.

Fuzzing the kernel is trickier, so we thought of two choices:

  • Syzkaller, an unsupervised protection guided kernel fuzzer

  • A normal goal protection guided fuzzer, resembling AFL

Syzkaller has been efficient at discovering Linux kernel vulnerabilities, however shouldn’t be appropriate for AIxCC as a result of Syzkaller generates sequences of syscalls to fuzz the entire Linux kernel, whereas AIxCC kernel challenges (exemplar) include a userspace harness to train particular components of the kernel. 

As an alternative, we selected to make use of AFL, which is often used to fuzz userspace packages. To allow kernel fuzzing, we adopted the same method to an older weblog put up from Cloudflare. We compiled the kernel with KCOV and KSAN instrumentation and ran it virtualized below QEMU. Then, a userspace harness acts as a faux AFL forkserver, which executes the inputs by executing the sequence of syscalls to be fuzzed. 

After each enter execution, the harness learn the KCOV protection and saved it in AFL’s protection counters by way of shared reminiscence to allow coverage-guided fuzzing. The harness additionally checked the kernel dmesg log after each run to find whether or not or not the enter prompted a KASAN sanitizer to set off.

Some adjustments to Cloudflare’s harness had been required to ensure that this to be pluggable with the offered kernel challenges. We wanted to show the harness right into a library/wrapper that might be linked in opposition to arbitrary AIxCC kernel harnesses.

AIxCC challenges include their very own predominant() which takes in a file path. The principle() operate opens and reads this file, and passes it to the harness() operate, which takes in a buffer and dimension representing the enter. We made our wrapper work by wrapping the predominant() throughout compilation by way of $CC -Wl,–wrap=predominant harness.c harness_wrapper.a  

The wrapper begins by organising KCOV, the AFL forkserver, and shared reminiscence. The wrapper additionally reads the enter from stdin (which is what AFL expects by default) and passes it to the harness() operate within the problem harness. 

As a result of AIxCC’s harnesses aren’t inside our management and should misbehave, we needed to be cautious with reminiscence or FD leaks throughout the problem harness. Certainly, the offered harness has numerous FD leaks, which signifies that fuzzing it can in a short time turn out to be ineffective because the FD restrict is reached.

To deal with this, we might both:

  • Forcibly shut FDs created through the working of harness by checking for newly created FDs by way of /proc/self/fd earlier than and after the execution of the harness, or

  • Simply fork the userspace harness by truly forking within the forkserver. 

The primary method labored for us. The latter is probably going most dependable, however could worsen efficiency.

All of those efforts enabled afl-fuzz to fuzz the Linux exemplar, however the vulnerability can’t be simply discovered even after hours of fuzzing, except supplied with seed inputs near the answer.


Bettering fuzzing with AI

This limitation of fuzzing highlights a possible space for opponents to discover AI’s capabilities. The enter format being sophisticated, mixed with gradual execution speeds make the precise reproducer onerous to find. Utilizing AI might unlock the power for fuzzing to search out this vulnerability shortly—for instance, by asking an LLM to generate seed inputs (or a script to generate them) near anticipated enter format primarily based on the harness supply code. Opponents would possibly discover inspiration in some attention-grabbing experiments carried out by Brendan Dolan-Gavitt from NYU, which present promise for this concept.

One different to fuzzing to search out vulnerabilities is to make use of static evaluation. Static evaluation historically has challenges with producing excessive quantities of false positives, in addition to difficulties in proving exploitability and reachability of points it factors out. LLMs might assist dramatically enhance bug discovering capabilities by augmenting conventional static evaluation methods with elevated accuracy and evaluation capabilities.

As soon as fuzzing finds a reproducer, we will produce key proof required for the PoU:

  1. The wrongdoer commit, which could be discovered from git historical past bisection.

  2. The anticipated sanitizer, which could be discovered by working the reproducer to get the crash and parsing the ensuing stacktrace.

As soon as the wrongdoer commit has been recognized, one apparent technique to “patch” the vulnerability is to simply revert this commit. Nonetheless, the commit could embody authentic adjustments which might be essential for performance checks to go. To make sure performance doesn’t break, we might apply delta debugging: we progressively attempt to embody/exclude totally different components of the wrongdoer commit till each the vulnerability not triggers, but all performance checks nonetheless go.

It is a fairly brute power method to “patching.” There is no such thing as a comprehension of the code being patched and it’ll probably not work for extra sophisticated patches that embody delicate adjustments required to repair the vulnerability with out breaking performance. 

Bettering patching with AI

These limitations spotlight a second space for opponents to use AI’s capabilities. One method is likely to be to make use of an LLM to recommend patches. A 2024 whitepaper from Google walks by way of one technique to construct an LLM-based automated patching pipeline.

Opponents might want to handle the next challenges:

  • Validating the patches by working crashes and checks to make sure the crash was prevented and the performance was not impacted

  • Narrowing prompts to incorporate solely the capabilities current within the crashing stack hint, to suit immediate limitations

  • Constructing a validation step to filter out invalid patches

Utilizing an LLM agent is probably going one other promising method, the place opponents might mix an LLM’s era capabilities with the power to compile and obtain debug take a look at failures or stacktraces iteratively.

Collaboration is important to harness the ability of AI as a widespread instrument for defenders. As developments emerge, we’ll combine them into OSS-Fuzz, that means that the outcomes from AIxCC will instantly enhance safety for the open supply ecosystem. We’re wanting ahead to the modern options that outcome from this competitors!