Improper access control in firmware for some Intel(R) PROSet/Wireless WiFi software for Windows before version 22.220 HF (Hot Fix) may allow a privileged user to potentially enable escalation of privilege via local access.

CVE-2023-28714

PendingIntent hijacking in WifiGeofenceManager prior to SMR Aug-2023 Release 1 allows local attacker to arbitrary file access.

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CVE-2023-30701: Samsung Mobile Security

An issue was discovered in Avira Phantom VPN through 2.23.1 for macOS. The VPN client insecurely configures the operating system such that all IP traffic to the VPN server's IP address is sent in plaintext outside the VPN tunnel, even if this traffic is not generated by the VPN client, while simultaneously using plaintext DNS to look up the VPN server's IP address. This allows an adversary to trick the victim into sending traffic to arbitrary IP addresses in plaintext outside the VPN tunnel. NOTE: the tunnelcrack.mathyvanhoef.com website uses this CVE ID to refer more generally to "ServerIP attack, combined with DNS spoofing, that can leak traffic to an arbitrary IP address" rather than to only Avira Phantom VPN.

CVE-2023-36673: Widespread design flaws in VPN clients

By Habiba Rashid
Dreams of Science Fiction Realized: ETH Researchers Demonstrate "Inception" Attack on CPUs.
This is a post from HackRead.com Read the original post: Novel ‘Inception’ Attack Exposes Sensitive Data in CPUs

*   **ETH Zurich researchers reveal ‘Inception’ attack akin to planting ideas in CPUs.**
*   **Attackers manipulate CPUs’ predictive algorithms to execute specific commands and access sensitive data.**
*   **Vulnerability impacts CPUs by AMD, bypassing security measures and compromising data integrity.**
*   **Researchers notify AMD in advance, allowing development of a patch to mitigate the threat.**
*   **Discovery prompts concerns about broader CPU vulnerabilities and potential impacts on cloud computing.**

In a development reminiscent of the mind-bending plot of the movie “Inception,” a team of researchers at ETH Zurich, led by Professor Kaveh Razavi, has successfully demonstrated a novel cybersecurity vulnerability that allows attackers to plant an idea within a computer’s central processing unit (CPU).

This revelation, which has been presented at the USENIX Security 2023 conference, has far-reaching implications for data security and cloud computing.

This discovery shows that certain CPUs can be manipulated into executing specific commands, effectively bypassing security measures and retrieving sensitive information. This novel attack, aptly named the “Inception” attack, revolves around the CPU’s inherent need to make guesses during program execution.

Modern CPUs operate at astounding speeds, making hundreds of millions of decisions per second during program execution. However, data transfer speeds from computer memory (DRAM) to the CPU have struggled to keep pace with this acceleration. To mitigate these delays, CPUs rely on predictive algorithms to anticipate the most likely next steps based on historical data, thereby significantly reducing processing time.

This reliance on predictions creates a vulnerability that attackers can exploit. Similar to the 2018 Spectre attack, where attackers leveraged mispredictions for unauthorized access, the Inception attack involves manipulating the CPU’s look-up table, which stores past instructions and predictions.

In this groundbreaking research shared with Hackread.com, ETH Zurich’s team, led by Professor Razavi, delved into vulnerabilities in CPUs manufactured by AMD. By capitalizing on the CPUs’ inclination to treat erroneous instructions as familiar, the researchers managed to implant an idea into the CPU’s memory during its predictive processes.

Consequently, the protective security features designed to validate the accuracy of predictions were bypassed, enabling the researchers to access sensitive data, including hashed root passwords.

The image provided to Hackread.com shows the devices and hardware used by researchers in the attack. (Photograph: Kaveh Razavi / ETH Zurich)

Professor Razavi’s team alerted AMD to the vulnerability in February, providing the company time to develop a patch before the research was made public. This vulnerability has been assigned the code CVE-2023-20569.

The discovery of the Inception attack poses a significant threat, particularly in the realm of cloud computing, where multiple customers share the same hardware infrastructure. With the potential to compromise data integrity across various virtual environments, cloud providers and hardware manufacturers will need to reconsider their security protocols.

Professor Razavi’s research also raises questions about the broader landscape of CPU vulnerabilities. While this specific attack targeted AMD CPUs, there is a looming concern about the possibility of similar attacks on CPUs from other manufacturers.

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I’m a student and cybersecurity writer. On a random Sunday, I am likely to be figuring out life and reading Kafka.

Novel ‘Inception’ Attack Exposes Sensitive Data in CPUs

TOTOLINK T10_v2 5.9c.5061_B20200511 has a stack-based buffer overflow in setWiFiWpsConfig in /lib/cste_modules/wps.so. Attackers can send crafted data in an MQTT packet, via the pin parameter, to control the return address and execute code.

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CVE-2023-40041: IoT_vuln/TOTOLINK/T10_V2/lib-cste_modules-wps.md at main · Korey0sh1/IoT_vuln

Tenda AC7 V1.0,V15.03.06.44, F1203 V2.0.1.6, AC5 V1.0,V15.03.06.28, AC9 V3.0,V15.03.06.42_multi and FH1205 V2.0.0.7(775) were discovered to contain a stack overflow via the deviceId parameter in the addWifiMacFilter function.

CVE-2023-38930: IoT-Vulns/tenda/addWifiMacFilter/README.md at main · FirmRec/IoT-Vulns

Netgear XR300 v1.0.3.78 was discovered to contain multiple buffer overflows via the wla_ssid and wlg_ssid parameters at genie_ap_wifi_change.cgi.

CVE-2023-36499: IoT-Vulns/netgear/nvram_ssid/README.md at main · FirmRec/IoT-Vulns

Tenda F1203 V2.0.1.6, FH1203 V2.0.1.6 and FH1205 V2.0.0.7(775) were discovered to contain a stack overflow via the ssid parameter in the form_fast_setting_wifi_set function.

CVE-2023-38940: IoT-Vulns/tenda/form_fast_setting_wifi_set at main · FirmRec/IoT-Vulns

By Habiba Rashid
Revolutionary AI Model Predicts Keystrokes Through Sound: A New Wave of Acoustic Attacks.
This is a post from HackRead.com Read the original post: AI Model Listens to Typing, Potentially Compromising Sensitive Data

1.  **Cornell researchers unveil AI model decoding keyboard inputs from audio signals.**
2.  **New attack predicts keystrokes by analyzing acoustic signatures of different keys.**
3.  **Implications for data security and emergence of acoustic cyberattacks raise concerns.**
4.  **AI model achieves 93% accuracy, setting a new record for audio-based classification systems.**
5.  **Countermeasures like noise introduction can reduce accuracy, limiting widespread malicious use.**

Researchers at Cornell University have unveiled a groundbreaking deep-learning model capable of deciphering keyboard input solely from audio signals. This pioneering advancement, led by Joshua Harrison, Ehsan Toreini, and Maryam Mehrnezhad, has significant implications for data security and the emergence of acoustic cyberattacks.

In a recently published paper titled “A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards,” the Cornell team introduced an Artificial intelligence (AI) model capable of accurately predicting keystrokes by analyzing the unique acoustic signatures produced by different keys on a keyboard.

The technique involves training the model to associate specific audio patterns with corresponding characters, allowing it to virtually ‘listen’ to typing and transcribe it with astonishing accuracy.

Traditional cyberattacks often exploit software vulnerabilities or rely on phishing tactics, but this new wave of attacks leverages the physical characteristics of keyboards, emphasizing the significance of sound as a potential security vulnerability. The implications are far-reaching, as this method could compromise user passwords, conversations, messages, and other sensitive information.

The team’s research indicates that the model achieves an impressive accuracy rate of 93% when trained using Zoom recordings, marking a new record for audio-based classification systems. The process of training the model involves exposing it to multiple instances of each keystroke on a specific keyboard. The researchers utilized a MacBook Pro, pressing each of its 36 keys 25 times to develop a comprehensive dataset for training.

Screenshot (Arxiv)

Despite its remarkable potential, the AI model does come with certain limitations and vulnerabilities. Changing typing styles or utilizing touch typing can significantly reduce the model’s accuracy, dropping it to a range of 40% to 64%. Additionally, countermeasures like introducing noise to the audio signal can obfuscate the keystrokes and diminish the model’s accuracy.

However, the researchers are clear that the model’s effectiveness is dependent on the specific keyboard’s sound profile. This dependence restricts the applicability of the attack to keyboards with similar acoustic characteristics, limiting its scope for widespread malicious use.

As the digital landscape evolves, the arms race between cyberattacks and defence measures continues to escalate. The development of AI-based acoustic side-channel attacks underscores the need for enhanced security measures, including innovative noise-cancellation solutions like NVIDIA’s RTX Broadcast, which can counteract these types of attacks.

For a comprehensive exploration of the Cornell team’s findings and methodologies, readers can refer to the official research paper (PDF) available for further study. As the boundaries of AI and cybersecurity continue to blur, understanding these advancements is crucial for individuals and organizations to stay ahead of potential threats.

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I’m a student and cybersecurity writer. On a random Sunday, I am likely to be figuring out life and reading Kafka.

AI Model Listens to Typing, Potentially Compromising Sensitive Data

In wlan service, there is a possible out of bounds read due to improper input validation. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07460540; Issue ID: ALPS07460540.

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