A vulnerability in some Netgear routers could allow an attacker with access to your network to run malicious commands on the device.
Source: ZDNet Security · 2026-10-30

A vulnerability in some Netgear routers could allow an attacker with access to your network to run malicious commands on the device.
Source: ZDNet Security · 2026-10-30

General Motors is taking what looks like a light slap on the wrist from a coalition of 35 states and the District of Columbia after agreeing to settle allegations that it collected drivers’ data without proper consent and sold it to data brokers that supplied insurance companies. The Washington state AG’s office said on Thursday that GM had reached a $220 million settlement with 36 attorneys general over the automaker’s collection and sharing of driving data, including precise location information, speed, seatbelt use, and other metrics. According to investigators, GM sold that data to brokers, which in turn supplied it to insurance companies that could use it to set customers’ premiums, without drivers’ informed consent. For reference, the $220 million settlement, which will be distributed among the participating jurisdictions, equates to less than 7 percent of GM’s $3.3 billion in net income attributable to stockholders for its third quarter, which ended on September 30, 2026. Don’t spend it all in one place, y’all. The settlement comes more than two years after a 2024 New York Times investigation found that GM had been selling the data collected from vehicle owners through its Smart Driver program. That program, part of GM subsidiary OnStar’s offering, collected the aforementioned data and handed it back to vehicle owners in reports designed to inform better driving habits. Unbeknownst to Smart Driver subscribers, their data was also being forked over to data brokers despite GM telling customers it wouldn’t share that information with insurers without their express consent. The insurers ultimately obtained the data by purchasing it from brokers with which GM had struck deals. To make matters worse, the Washington AG’s office said GM incentivized dealerships to enroll customers in Smart Driver, with employees in some cases accepting terms and conditions on customers’ behalf without their knowledge or consent. GM also failed to notify many drivers that their information was being collected and shared. Its OnStar app incorrectly told more than 370,000 drivers they were not enrolled in Smart Driver, even as GM continued collecting and selling their data, according to investigators. “The data GM sold was personal, identifiable, and specific,” the AG’s office said. GM canned the Smart Driver program in April 2024, just over a month after the New York Times exposed its data-sharing practices, citing customer feedback. In addition to the monetary pittance it’s handing over to the states, GM also agreed with the 36 AGs to limit the driving data it collects, better protect customer PII, and establish a “comprehensive data privacy program.” The Washington AG’s office described its settlement as “significantly stronger than any other resolution reached with GM for this conduct.” Speaking of those other terms, GM finalized its settlement of the same allegations with the Federal Trade Commission in January 2026. The FTC order required GM to obtain drivers’ express consent before collecting, using, or sharing covered driving data, subject to certain exceptions. It also required the company to offer a simple way for drivers to request a copy of their data, request its deletion, and disable precise geolocation data collection where their vehicles support it. The state of California separately reached a $12.75 million settlement with GM in the matter in May; a separate case in Texas is ongoing. “This agreement … reinforces steps we’ve taken to strengthen our privacy practices,” a GM spokesperson told The Register, though the company noted that it still stands by driver data collection as a best practice. “Safer, more helpful and personalized experiences are enabled by connected vehicle data, and GM is committed to being clear about what information is used, how it’s protected, and the choices customers have about what they share,” the spokesperson added. ®
Source: The Register — Security · 2026-10-30

General Motors is taking what looks like a light slap on the wrist from a coalition of 35 states and the District of Columbia after agreeing to settle allegations that it collected drivers’ data without proper consent and sold it to data brokers that supplied insurance companies. The Washington state AG’s office said on Thursday that GM had reached a $220 million settlement with 36 attorneys general over the automaker’s collection and sharing of driving data, including precise location information, speed, seatbelt use, and other metrics. According to investigators, GM sold that data to brokers, which in turn supplied it to insurance companies that could use it to set customers’ premiums, without drivers’ informed consent. For reference, the $220 million settlement, which will be distributed among the participating jurisdictions, equates to less than 7 percent of GM’s $3.3 billion in net income attributable to stockholders for its third quarter, which ended on September 30, 2026. Don’t spend it all in one place, y’all. The settlement comes more than two years after a 2024 New York Times investigation found that GM had been selling the data collected from vehicle owners through its Smart Driver program. That program, part of GM subsidiary OnStar’s offering, collected the aforementioned data and handed it back to vehicle owners in reports designed to inform better driving habits. Unbeknownst to Smart Driver subscribers, their data was also being forked over to data brokers despite GM telling customers it wouldn’t share that information with insurers without their express consent. The insurers ultimately obtained the data by purchasing it from brokers with which GM had struck deals. To make matters worse, the Washington AG’s office said GM incentivized dealerships to enroll customers in Smart Driver, with employees in some cases accepting terms and conditions on customers’ behalf without their knowledge or consent. GM also failed to notify many drivers that their information was being collected and shared. Its OnStar app incorrectly told more than 370,000 drivers they were not enrolled in Smart Driver, even as GM continued collecting and selling their data, according to investigators. “The data GM sold was personal, identifiable, and specific,” the AG’s office said. GM canned the Smart Driver program in April 2024, just over a month after the New York Times exposed its data-sharing practices, citing customer feedback. In addition to the monetary pittance it’s handing over to the states, GM also agreed with the 36 AGs to limit the driving data it collects, better protect customer PII, and establish a “comprehensive data privacy program.” The Washington AG’s office described its settlement as “significantly stronger than any other resolution reached with GM for this conduct.” Speaking of those other terms, GM finalized its settlement of the same allegations with the Federal Trade Commission in January 2026. The FTC order required GM to obtain drivers’ express consent before collecting, using, or sharing covered driving data, subject to certain exceptions. It also required the company to offer a simple way for drivers to request a copy of their data, request its deletion, and disable precise geolocation data collection where their vehicles support it. The state of California separately reached a $12.75 million settlement with GM in the matter in May; a separate case in Texas is ongoing. “This agreement … reinforces steps we’ve taken to strengthen our privacy practices,” a GM spokesperson told The Register, though the company noted that it still stands by driver data collection as a best practice. “Safer, more helpful and personalized experiences are enabled by connected vehicle data, and GM is committed to being clear about what information is used, how it’s protected, and the choices customers have about what they share,” the spokesperson added. ®
Source: The Register Security · 2026-10-30
Hardware wallet device maker Trezor has received credible reports about a suspected hardware crypto-theft implant in a Trezor Safe 5 sold by its reseller CryptoBilis in Southeast Asia. […]
Source: BleepingComputer · 2026-10-30
A malicious version of Anthropic’s Claude Code VS Code extension was distributed through Cursor’s extension marketplace for approximately 11 hours on October 29, exposing up to 5,000 developers to malware designed to install persistent SSH backdoors. […]
Source: BleepingComputer · 2026-10-30
A fake GitHub repository tricks AI coding agents into installing SolanaCry malware, giving attackers remote access to developers’ Windows PCs through NetSupport.
Source: HackRead · 2026-10-30
Flaws in SS7 and Diameter signaling protocols can allow attackers to track phone locations, intercept calls and SMS verification codes, and impersonate subscribers without compromising their devices. Cape explains how these attacks exploit trust between networks and how its security-focused mobile service helps defend against them. […]
Source: BleepingComputer · 2026-10-30
Attackers have started exploiting a maximum-severity GitLab vulnerability patched earlier this month, according to the US Cybersecurity and Infrastructure Security Agency (CISA). […]
Source: BleepingComputer · 2026-10-30
Remediation prioritization after a pentest is the process of ranking and ordering security findings based on their risk, exploitability, and business impact. A penetration test can produce any number of findings, ranging from the very low (zero) to the very high (hundreds).
The security of the tested organization depends on the findings management processes addressing the most important results first.
When prioritizing pentest results, some practitioners rely solely on frameworks like the Common Vulnerability Scoring System (CVSS). The logic is sound: the higher a vulnerability’s CVSS score is, the more risk it poses, and the sooner you should fix it, right?
Unfortunately, the reality is much more complicated. Boiling down a complex ecosystem of risk into a one-dimensional number means ignoring the wider business context. In the end, that means you fix vulnerabilities that pose serious theoretical risk first, while genuine exposures get pushed to the back of the queue.
Lets use two hypothetical vulnerabilities as an example.
Now think about which one you would fix first.
Would you prioritize the“critical” flaw on an intranet site, containing no sensitive data that attackers could only reach if they’d already compromised your internal network? Or would you prioritize the “medium” flaw on an API that processes live credit card transactions, and that any script kiddie could manipulate?
Across an entire organization, or multiple organizations if you’re a consultant or MSP, those critical flaws can really pile up. In one AI pentesting survey of 158 base practitioners by Pentest-Tools.com, conducted in June 2026, 68.4% of respondents said a 500+ candidate finding set would strain the team or be unmanageable.
Approaching risk prioritization by trying to fix every “Critical” finding is going to exhaust you and your team. That’s when people make mistakes, and you end up with a major incident on your hands.
That same distinction, in part, separates a candidate finding from a validated one. For example, vulnerability with a high CVSS score but near-zero exploitability should remain a candidate. To graduate a vulnerability from a candidate to a legally and technically validated finding, it must pass through a strict filter that involves three processes.
The first step is to eliminate as much administrative noise as possible. That means deduplicating findings and removing any that are obviously non-exploitable.
Then, you need to confirm exploitability. That typically means executing a safe, controlled attack path against the target environment to prove that a state change, data exposure, or privilege escalation can actually occur.
Finally, you need concrete evidence. A validated finding cannot rely on a generic tool’s “confidence score.” It must include comprehensive proof that a developer or auditor can immediately follow:
Now, many vendors are going to claim that AI-generated findings ease the strain of exploit validation. In many cases, they’re telling the truth. But as with any AI technology, a healthy level of skepticism is important. According to the same study, 81.7% of respondents encounter AI-generated findings that need significant manual exploit validation at least sometimes.
Together, CVSS, Exploit Prediction Scoring System (EPSS) scores, and business context create a single risk ranking that reflects real-world danger to the tested organization.
CVSS, as mentioned earlier, measures theoretical severity. It tells you how bad a flaw is in a vacuum, but is entirely blind to context. EPSS measures probability. It tells you how likely an attacker is to exploit a flaw in the wild over the next 30 days, but doesn’t measure the potential impact of an exploitation.
Business context measures operational impact. It tells you the value of the affected asset, how easily an attacker can reach a flaw, and what they can steal. It tells you nothing, however, about the threat itself. Ultimately, business context is expressed as residual risk, the danger left over after you factor in your existing defenses. Calculating it relies on measuring four factors:
By checking these boxes, you won’t waste time fixing a “Critical” bug locked in an empty, disconnected server. Instead, you’ll prioritize the “Medium” bug on your public checkout page that hackers are actively trying to breach.
Sometimes, you’re not going to have much time for remediation. That’s natural. In those situations, prioritize validated findings with high threat activity (EPSS score) and critical business exposure. They’re the findings that pose the most threat to your organization.
Prioritization done well is itself evidence. A ranked, validated list, with a written reason for what was deprioritized, is what a team can defend later.
You’ll need to record the owner (the specific individual accepting the risk), the date of the decision, the technical operational basis for deprioritization (such as an active compensating control), and a hard review date for re-evaluation.
A prioritized ranking list counts as valid audit evidence because it proves compliance alignment with several major security frameworks. For example:
A ranked, validated list also gives MSPs and MSSPs a clear, auditable record of truth that clients can either accept or challenge with their own evidence. This transparency ensures both sides agree on which real-world risks need to be addressed first.
A defensible ranking record contains the validated asset identification, its technical CVSS severity, its real-world EPSS probability, the active compensating controls defining its residual list, the formal sign-off from the risk owner, and the targeted review schedule.
About the Author: Josh Breaker-Rolfe is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
Follow me on Twitter: @securityaffairs and Facebook and Mastodon
(SecurityAffairs – hacking, pentest)
Source: Security Affairs · 2026-10-30
Binance probes a DevOps engineer’s hacked laptop after a $6.3 billion theft linked by researchers to North Korean hackers, and promises to cover all customer losses.
Source: HackRead · 2026-10-30