Entries Tagged "backdoors"

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New Revelations from the Snowden Documents

Jake Appelbaum’s PhD thesis contains several new revelations from the classified NSA documents provided to journalists by Edward Snowden. Nothing major, but a few more tidbits.

Kind of amazing that that all happened ten years ago. At this point, those documents are more historical than anything else.

And it’s unclear who has those archives anymore. According to Appelbaum, The Intercept destroyed their copy.

I recently published an essay about my experiences ten years ago.

Posted on September 21, 2023 at 7:03 AM • View Comments

Microsoft Signing Key Stolen by Chinese

A bunch of networks, including US Government networks, have been hacked by the Chinese. The hackers used forged authentication tokens to access user email, using a stolen Microsoft Azure account consumer signing key. Congress wants answers. The phrase “negligent security practices” is being tossed about—and with good reason. Master signing keys are not supposed to be left around, waiting to be stolen.

Actually, two things went badly wrong here. The first is that Azure accepted an expired signing key, implying a vulnerability in whatever is supposed to check key validity. The second is that this key was supposed to remain in the the system’s Hardware Security Module—and not be in software. This implies a really serious breach of good security practice. The fact that Microsoft has not been forthcoming about the details of what happened tell me that the details are really bad.

I believe this all traces back to SolarWinds. In addition to Russia inserting malware into a SolarWinds update, China used a different SolarWinds vulnerability to break into networks. We know that Russia accessed Microsoft source code in that attack. I have heard from informed government officials that China used their SolarWinds vulnerability to break into Microsoft and access source code, including Azure’s.

I think we are grossly underestimating the long-term results of the SolarWinds attacks. That backdoored update was downloaded by over 14,000 networks worldwide. Organizations patched their networks, but not before Russia—and others—used the vulnerability to enter those networks. And once someone is in a network, it’s really hard to be sure that you’ve kicked them out.

Sophisticated threat actors are realizing that stealing source code of infrastructure providers, and then combing that code for vulnerabilities, is an excellent way to break into organizations who use those infrastructure providers. Attackers like Russia and China—and presumably the US as well—are prioritizing going after those providers.

News articles.

EDITED TO ADD: Commentary:

This is from Microsoft’s explanation. The China attackers “acquired an inactive MSA consumer signing key and used it to forge authentication tokens for Azure AD enterprise and MSA consumer to access OWA and Outlook.com. All MSA keys active prior to the incident—including the actor-acquired MSA signing key—have been invalidated. Azure AD keys were not impacted. Though the key was intended only for MSA accounts, a validation issue allowed this key to be trusted for signing Azure AD tokens. The actor was able to obtain new access tokens by presenting one previously issued from this API due to a design flaw. This flaw in the GetAccessTokenForResourceAPI has since been fixed to only accept tokens issued from Azure AD or MSA respectively. The actor used these tokens to retrieve mail messages from the OWA API.”

Posted on August 7, 2023 at 7:03 AM • View Comments

Backdoor in TETRA Police Radios

Seems that there is a deliberate backdoor in the twenty-year-old TErrestrial Trunked RAdio (TETRA) standard used by police forces around the world.

The European Telecommunications Standards Institute (ETSI), an organization that standardizes technologies across the industry, first created TETRA in 1995. Since then, TETRA has been used in products, including radios, sold by Motorola, Airbus, and more. Crucially, TETRA is not open-source. Instead, it relies on what the researchers describe in their presentation slides as “secret, proprietary cryptography,” meaning it is typically difficult for outside experts to verify how secure the standard really is.

The researchers said they worked around this limitation by purchasing a TETRA-powered radio from eBay. In order to then access the cryptographic component of the radio itself, Wetzels said the team found a vulnerability in an interface of the radio.

[…]

Most interestingly is the researchers’ findings of what they describe as the backdoor in TEA1. Ordinarily, radios using TEA1 used a key of 80-bits. But Wetzels said the team found a “secret reduction step” which dramatically lowers the amount of entropy the initial key offered. An attacker who followed this step would then be able to decrypt intercepted traffic with consumer-level hardware and a cheap software defined radio dongle.

Looks like the encryption algorithm was intentionally weakened by intelligence agencies to facilitate easy eavesdropping.

Specifically on the researchers’ claims of a backdoor in TEA1, Boyer added “At this time, we would like to point out that the research findings do not relate to any backdoors. The TETRA security standards have been specified together with national security agencies and are designed for and subject to export control regulations which determine the strength of the encryption.”

And I would like to point out that that’s the very definition of a backdoor.

Why aren’t we done with secret, proprietary cryptography? It’s just not a good idea.

Details of the security analysis. Another news article.

Posted on July 26, 2023 at 7:05 AM • View Comments

Another Malware with Persistence

Here’s a piece of Chinese malware that infects SonicWall security appliances and survives firmware updates.

On Thursday, security firm Mandiant published a report that said threat actors with a suspected nexus to China were engaged in a campaign to maintain long-term persistence by running malware on unpatched SonicWall SMA appliances. The campaign was notable for the ability of the malware to remain on the devices even after its firmware received new firmware.

“The attackers put significant effort into the stability and persistence of their tooling,” Mandiant researchers Daniel Lee, Stephen Eckels, and Ben Read wrote. “This allows their access to the network to persist through firmware updates and maintain a foothold on the network through the SonicWall Device.”

To achieve this persistence, the malware checks for available firmware upgrades every 10 seconds. When an update becomes available, the malware copies the archived file for backup, unzips it, mounts it, and then copies the entire package of malicious files to it. The malware also adds a backdoor root user to the mounted file. Then, the malware rezips the file so it’s ready for installation.

“The technique is not especially sophisticated, but it does show considerable effort on the part of the attacker to understand the appliance update cycle, then develop and test a method for persistence,” the researchers wrote.

Posted on March 9, 2023 at 8:33 PM • View Comments

Putting Undetectable Backdoors in Machine Learning Models

This is really interesting research from a few months ago:

Abstract: Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. Delegation of learning has clear benefits, and at the same time raises serious concerns of trust. This work studies possible abuses of power by untrusted learners.We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key,” the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.

First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given query access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Moreover, even if the distinguisher can request backdoored inputs of its choice, they cannot backdoor a new input­a property we call non-replicability.

Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm (Rahimi, Recht; NeurIPS 2007). In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor. The backdooring algorithm executes the RFF algorithm faithfully on the given training data, tampering only with its random coins. We prove this strong guarantee under the hardness of the Continuous Learning With Errors problem (Bruna, Regev, Song, Tang; STOC 2021). We show a similar white-box undetectable backdoor for random ReLU networks based on the hardness of Sparse PCA (Berthet, Rigollet; COLT 2013).

Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, by constructing undetectable backdoor for an “adversarially-robust” learning algorithm, we can produce a classifier that is indistinguishable from a robust classifier, but where every input has an adversarial example! In this way, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.

Turns out that securing ML systems is really hard.

Posted on February 24, 2023 at 7:34 AM • View Comments

Manipulating Weights in Face-Recognition AI Systems

Interesting research: “Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons“:

Abstract: In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other’s existence with almost no interference.

We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of 99.35%. When we tried to individually anonymize ten celebrities, the network failed to recognize two of their images as being the same person in 96.97% to 98.29% of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in 91.51% of the time. For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all ten celebrities on the same model reduced the success rate for each celebrity by no more than 0.91%). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than 0.48% (and in most cases, it remained above 99.30%).

It’s a weird attack. On the one hand, the attacker has access to the internals of the facial recognition system. On the other hand, this is a novel attack in that it manipulates internal weights to achieve a specific outcome. Given that we have no idea how those weights work, it’s an important result.

Posted on February 3, 2023 at 7:07 AM • View Comments

Trojaned Windows Installer Targets Ukraine

Mandiant is reporting on a trojaned Windows installer that targets Ukrainian users. The installer was left on various torrent sites, presumably ensnaring people downloading pirated copies of the operating system:

Mandiant uncovered a socially engineered supply chain operation focused on Ukrainian government entities that leveraged trojanized ISO files masquerading as legitimate Windows 10 Operating System installers. The trojanized ISOs were hosted on Ukrainian- and Russian-language torrent file sharing sites. Upon installation of the compromised software, the malware gathers information on the compromised system and exfiltrates it. At a subset of victims, additional tools are deployed to enable further intelligence gathering. In some instances, we discovered additional payloads that were likely deployed following initial reconnaissance including the STOWAWAY, BEACON, and SPAREPART backdoors.

One obvious solution would be for Microsoft to give the Ukrainians Windows licenses, so they don’t have to get their software from sketchy torrent sites.

Posted on December 20, 2022 at 7:30 AM • View Comments

Inserting a Backdoor into a Machine-Learning System

Interesting research: “ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks, by Tim Clifford, Ilia Shumailov, Yiren Zhao, Ross Anderson, and Robert Mullins:

Abstract: Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors in models or even remove them. These defences work by inspecting the training data, the model, or the integrity of the training procedure. In this work, we show that backdoors can be added during compilation, circumventing any safeguards in the data preparation and model training stages. As an illustration, the attacker can insert weight-based backdoors during the hardware compilation step that will not be detected by any training or data-preparation process. Next, we demonstrate that some backdoors, such as ImpNet, can only be reliably detected at the stage where they are inserted and removing them anywhere else presents a significant challenge. We conclude that machine-learning model security requires assurance of provenance along the entire technical pipeline, including the data, model architecture, compiler, and hardware specification.

Ross Anderson explains the significance:

The trick is for the compiler to recognise what sort of model it’s compiling—whether it’s processing images or text, for example—and then devising trigger mechanisms for such models that are sufficiently covert and general. The takeaway message is that for a machine-learning model to be trustworthy, you need to assure the provenance of the whole chain: the model itself, the software tools used to compile it, the training data, the order in which the data are batched and presented—in short, everything.

Posted on October 11, 2022 at 7:18 AM • View Comments

Symbiote Backdoor in Linux

Interesting:

What makes Symbiote different from other Linux malware that we usually come across, is that it needs to infect other running processes to inflict damage on infected machines. Instead of being a standalone executable file that is run to infect a machine, it is a shared object (SO) library that is loaded into all running processes using LD_PRELOAD (T1574.006), and parasitically infects the machine. Once it has infected all the running processes, it provides the threat actor with rootkit functionality, the ability to harvest credentials, and remote access capability.

News article:

Researchers have unearthed a discovery that doesn’t occur all that often in the realm of malware: a mature, never-before-seen Linux backdoor that uses novel evasion techniques to conceal its presence on infected servers, in some cases even with a forensic investigation.

No public attribution yet.

So far, there’s no evidence of infections in the wild, only malware samples found online. It’s unlikely this malware is widely active at the moment, but with stealth this robust, how can we be sure?

Posted on June 22, 2022 at 6:07 AM • View Comments

New Sophisticated Malware

Mandiant is reporting on a new botnet.

The group, which security firm Mandiant is calling UNC3524, has spent the past 18 months burrowing into victims’ networks with unusual stealth. In cases where the group is ejected, it wastes no time reinfecting the victim environment and picking up where things left off. There are many keys to its stealth, including:

  • The use of a unique backdoor Mandiant calls Quietexit, which runs on load balancers, wireless access point controllers, and other types of IoT devices that don’t support antivirus or endpoint detection. This makes detection through traditional means difficult.
  • Customized versions of the backdoor that use file names and creation dates that are similar to legitimate files used on a specific infected device.
  • A live-off-the-land approach that favors common Windows programming interfaces and tools over custom code with the goal of leaving as light a footprint as possible.
  • An unusual way a second-stage backdoor connects to attacker-controlled infrastructure by, in essence, acting as a TLS-encrypted server that proxies data through the SOCKS protocol.

[…]

Unpacking this threat group is difficult. From outward appearances, their focus on corporate transactions suggests a financial interest. But UNC3524’s high-caliber tradecraft, proficiency with sophisticated IoT botnets, and ability to remain undetected for so long suggests something more.

From Mandiant:

Throughout their operations, the threat actor demonstrated sophisticated operational security that we see only a small number of threat actors demonstrate. The threat actor evaded detection by operating from devices in the victim environment’s blind spots, including servers running uncommon versions of Linux and network appliances running opaque OSes. These devices and appliances were running versions of operating systems that were unsupported by agent-based security tools, and often had an expected level of network traffic that allowed the attackers to blend in. The threat actor’s use of the QUIETEXIT tunneler allowed them to largely live off the land, without the need to bring in additional tools, further reducing the opportunity for detection. This allowed UNC3524 to remain undetected in victim environments for, in some cases, upwards of 18 months.

Posted on May 4, 2022 at 6:15 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.