As mentioned earlier, the Flipper Zero has a built-in sub-GHz radio that lets the device receive data (or transmit it, with the right firmware in approved regions) on the same wireless frequencies as keyfobs and other devices. Most traffic preemption devices intended for emergency traffic redirection don’t actually transmit signals over RF. Instead, they use optical technology to beam infrared light from vehicles to static receivers mounted on traffic light poles.

Perhaps the most well-known branding for these types of devices is called Opticom. Essentially, the tech works by detecting a specific pattern of infrared light emitted by the Mobile Infrared Transmitter (MIRT) installed in a police car, fire truck, or ambulance when the MIRT is switched on. When the receiver detects the light, the traffic system then initiates a signal change as the emergency vehicle approaches an intersection, safely redirecting the traffic flow so that the emergency vehicle can pass through the intersection as if it were regular traffic and potentially avoid a collision.

This seems easy to do, but it’s also very illegal. It’s called “impersonating an emergency vehicle,” and it comes with hefty penalties if you’re caught.

The Anti-Theft Mode feature will make the devices invisible to Scan and Secure, the company’s in-app feature that lets you know if any nearby Tiles are following you. But to activate the new Anti-Theft Mode, the Tile owner will have to verify their real identity with a government-issued ID, submit a biometric scan that helps root out fake IDs, agree to let Tile share their information with law enforcement and agree to be subject to a $1 million penalty if convicted in a court of law of using Tile for criminal activity. So although it technically makes the device easier for stalkers to use Tiles silently, it makes the penalty of doing so high enough to (at least in theory) deter them from trying.

Interesting theory. But it won’t work against attackers who don’t have any money.

Hulls believes the approach is superior to Apple’s solution with AirTag, which emits a sound and notifies iPhone users that one of the trackers is following them.

My complaint about the technical solutions is that they only work for users of the system. Tile security requires an “in-app feature.” Apple’s AirTag “notifies iPhone users.” What we need is a common standard that is implemented on all smartphones, so that people who don’t use the trackers can be alerted if they are being surveilled by one of them.

CISA has released a recovery script for organizations that have fallen victim to ESXiArgs ransomware. The ESXiArgs ransomware encrypts configuration files on vulnerable ESXi servers, potentially rendering virtual machines (VMs) unusable.

CISA recommends organizations impacted by ESXiArgs evaluate the script and guidance provided in the accompanying README file to determine if it is fit for attempting to recover access to files in their environment.

Organizations can access the recovery script here: https://github.com/cisagov/ESXiArgs-Recover

It’s been an exciting decade, filled with excellent employees, excellent clients, and excellent partners.

Last month I was honored to sit down with Caroline McCaffrey for what appears to be the inaugural episode of her new podcast, the vCISO chronicles. We talked through many things that seem relevant to our anniversary, so it’s timely that it was released this month.

Rather than blogging about it, just head over and give it a listen!

Thanks again, Caroline! We look forward to more!

And here’s to another 10 years for NESP!

CISA released six Industrial Control Systems (ICS) advisories on February 2, 2023.These advisories provides timely information about current security issues, vulnerabilities, and exploits surrounding ICS.

CISA encourages users and administrators to review the newly released ICS advisories for technical details and mitigations:

• ICSA-23-033-01 Delta Electronics DIAScreen
• ICSA-23-033-02 Mitsubishi Electric GOT2000 Series and GT SoftGOT2000
• ICSA-23-033-03 Baicells Nova
• ICSA-23-033-04 Delta Electronics DVW-W02W2-E2
• ICSA-23-033-05 Delta Electronics DX-2100-L1-CN
• ICSA-22-221-01 Mitsubishi Electric Multiple Factory Automation Products (Update D)

US law enforcement can access details of money transfers without a warrant through an obscure surveillance program the Arizona attorney general’s office created in 2014. A database stored at a nonprofit, the Transaction Record Analysis Center (TRAC), provides full names and amounts for larger transfers (above $500) sent between the US, Mexico and 22 other regions through services like Western Union, MoneyGram and Viamericas. The program covers data for numerous Caribbean and Latin American countries in addition to Canada, China, France, Malaysia, Spain, Thailand, Ukraine and the US Virgin Islands. Some domestic transfers also enter the data set.

[…]

You need to be a member of law enforcement with an active government email account to use the database, which is available through a publicly visible web portal. Leber told The Journal that there haven’t been any known breaches or instances of law enforcement misuse. However, Wyden noted that the surveillance program included more states and countries than previously mentioned in briefings. There have also been subpoenas for bulk money transfer data from Homeland Security Investigations (which withdrew its request after Wyden’s inquiry), the DEA and the FBI.

How is it that Arizona can be in charge of this?

Wall Street Journal podcast—with transcript—on the program. I think the original reporting was from last March, but I missed it back then.

The list is back in the news today, having been left exposed on an insecure airline computer. (The airline is CommuteAir, a company so obscure that I’ve never heard of it before.)

This is, of course, the problem with having to give a copy of your secret list to lots of people.

According to the complaint against him, Al-Azhari allegedly visited a dark web site that hosts “unofficial propaganda and photographs related to ISIS” multiple times on May 14, 2019. In virtue of being a dark web site—­that is, one hosted on the Tor anonymity network—­it should have been difficult for the site owner’s or a third party to determine the real IP address of any of the site’s visitors.

Yet, that’s exactly what the FBI did. It found Al-Azhari allegedly visited the site from an IP address associated with Al-Azhari’s grandmother’s house in Riverside, California. The FBI also found what specific pages Al-Azhari visited, including a section on donating Bitcoin; another focused on military operations conducted by ISIS fighters in Iraq, Syria, and Nigeria; and another page that provided links to material from ISIS’s media arm. Without the FBI deploying some form of surveillance technique, or Al-Azhari using another method to visit the site which exposed their IP address, this should not have been possible.

There are lots of ways to de-anonymize Tor users. Someone at the NSA gave a presentation on this ten years ago. (I wrote about it for the Guardian in 2013, an essay that reads so dated in light of what we’ve learned since then.) It’s unlikely that the FBI uses the same sorts of broad surveillance techniques that the NSA does, but it’s certainly possible that the NSA did the surveillance and passed the information to the FBI.

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Abstract: Advances in natural language generation (NLG) have resulted in machine generated text that is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools democratizing access to generative models are proliferating. The great potential of state-of-the-art NLG systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.