Analysis of 400,000 healthy adults finds 4% higher mortality risk among those taking the supplements
Taking a daily multivitamin does not help people to live any longer and may actually increase the risk of an early death, a major study has found.
Researchers in the US analysed health records from nearly 400,000 adults with no major long-term diseases to see whether daily multivitamins reduced their risk of death over the next two decades.
Alarm over high mortality and miscarriage rates as mutated virus spreads in eastern Democratic Republic of the Congo
A dangerous strain of mpox that is killing children and causing miscarriages in the Democratic Republic of the Congo is the most transmissible yet and could spread internationally, scientists have warned.
The virus appears to be spreading from person to person via both sexual and non-sexual contact, in places ranging from brothels to schools.
The Changβe-6 missionβs sample, which might hold clues about the origins of the moon and Earth, is the latest achievement of Chinaβs lunar exploration program.
Anyone who has had a urinary tract infection knows how agonising they can be. Some infections go away on their own, but many need antibiotics.
Beneath the surface of this very common infection lie many mysteries, unanswered questions, and unnecessary suffering. And it gets to the heart of the challenge of tackling antimicrobial resistance.
Madeleine Finlay speaks to Dr Jennifer Rohn, head of the centre for urological biology at University College London, about what we now understand about how UTIs take hold, and the complexity surrounding their treatment
An Austrian forest ranger picked up the rock in 1976. Decades later, scientists discovered the objectβs origin story while digging through old photos.
A self-test for H.I.V. in Harare, Zimbabwe. The every-six-months injection was found to provide better protection than the current oral drug for whatβs called pre-exposure prophylaxis, also taken as a daily pill.
In heat waves, chemicals like formaldehyde and ozone can form more readily in the air, according to researchers driving mobile labs in New York City this week.
By studying bees and their honey near decomposing human tissue, researchers at George Mason University hope to give crime scene investigators a new tool for finding the hidden dead.
Researchers at George Mason Universityβs new βbody farmβ in Northern Virginia hope to use bees to draw up a formula for human decomposition that investigators can use to narrow a search for human remains.
Dr. Hans Klingemann is the chief science officer at ImmunityBio, which develops immunotherapy drugs for people. He also explores whether the treatments might someday prolong dogsβ lives.
Macaques, reeling from a hurricane, learned by necessity to get along, a study found. Itβs one of the first to suggest that animals can adapt to environmental upheaval with social changes.
Agricultural insecticides were a key factor, according to a study focused on the Midwest, though researchers emphasized the importance of climate change and habitat loss.
Summary Eclypsium Automata, our automated binary analysis system, has identified a high impact vulnerability (CVE-2024-0762 with a reported CVSS of 7.5) in the Phoenix SecureCore UEFI firmware that runs on multiple families of Intel Core desktop and mobile processors. The issue involves an unsafe variable in the Trusted Platform Module (TPM) configuration that could lead [β¦]
There has long been anecdotal evidence of the wormy creatures taking to the air, but videos recorded in Madagascar at last prove the animalsβ acrobatics.
In high-tech labs, workers are generating data to train A.I. algorithms to design better medicine, faster. But the transformation is just getting underway.
Chips in a container at Terray Therapeutics in Monrovia, Calif. Each of the custom-made chips has millions of minuscule wells for measuring drug screening reactions quickly and accurately.
Titania, specialists in continuous network security and compliance assurance solutions, announced the release of compelling new research that highlights a significant shift in cybersecurity spending towards proactive security measures.
The report, "Emerging Best Practice in the Use of Proactive Security Solutions," indicates a marked increase in investments aimed at preemptively mitigating cyber threats.
According to the study, over 70% of businesses reported increased spending on proactive security solutions, such as attack surface management and risk-based vulnerability management, over the past year. This growth notably outpaces investments in both preventative and reactive measures.
Strategic Implementation and Cybersecurity Industry Trends
Conducted in partnership with Omdia, a global analyst and advisory leader, the study surveyed over 400 security decision-makers across North America, the UK, France, and Germany.
The findings highlight a rapid adoption of proactive security measures driven by three key objectives:
These proactive solutions are becoming an essential layer of protection, providing a comprehensive understanding of the threat landscape and attack surface to enhance organizational resilience and readiness.
Geographic and Sectoral Insights
The trend towards proactive security is particularly pronounced in the EMEA region, where 74% of respondents increased their budgets compared to 67% in North America. The financial services sector (54%) and critical infrastructure organizations, including energy and utilities companies (53%), show a strong inclination towards these investments.
Nearly half (47%) of the respondents reported that their top cybersecurity goals for the next 12-24 months include reducing the opportunity for threats through proactive security. In contrast, only 27% of organizations plan to focus on improving tactical outcomes such as better threat prevention, detection, and response.
Enhancing Security Posture
Organizations are increasingly recognizing the need to improve their security posture through proactive security tools, which significantly enhance attack surface management and security control optimization.
Many organizations reported limited visibility into the security posture of their network assets, such as firewalls, switches, and routers. Approximately half of the surveyed organizations check their network devices at most monthly, and some only monitor devices in critical segments or a sample of devices across their networks.
Critical infrastructure organizations reported lower confidence than other industries in their ability to maintain adequate network segmentation and prevent unauthorized network access.
Anticipated Organizational Impact
Almost half (48%) of all respondents anticipate a high level of organizational disruption due to the broader adoption of proactive security solutions, highlighting the transformative impact these measures are expected to have.
βThis research vividly illustrates a widespread and rapid shift towards proactive security to improve operational readiness and resilience,β said Tom Beese, Executive Chairman of Titania. βOrganizations recognize the critical need to stay ahead of known threats and shut down attacks by investing in solutions that offer real-time visibility of their security posture and remediation actions that continuously minimize their exposure.β
Businesses emphasized the importance of consolidating proactive security tools, with 65% highlighting better visibility and management of the attack surface, 60% focusing on improved security control optimization, and 54% noting manpower productivity improvements.
Critical Proactive Security Capabilities
The survey identified several critical proactive security capabilities:
The ability to view risks through different attack frameworks (61%).
Full asset context (60%).
Integration with existing security fabric to implement temporary mitigations (57%).
Andrew Braunberg, Principal Analyst at Omdia, explained, βWhile the cybersecurity industry has clung to the 'assume breach' mantra with its preventative and reactive solutions, organizations are awakening to a smarter strategy: proactively understanding attack surfaces, mapping attack paths, and plugging vulnerabilities to prevent breaches. Network device configurations are crucial to security posture management, and the adoption of proactive security solutions that automate configuration assessments could have a transformative impact.β
The report highlights a gap in industry guidance on best practices for building a proactive security strategy. It notes that the US Defense Departmentβs Command Cyber Readiness Inspection program (CORA) and the EUβs Digital Operational Resilience Act (DORA) requirements align well with the need for proactive security solutions.
Researchers from Cyble Research and Intelligence Labs (CRIL) have discovered a QR code-based phishing campaign that uses malicious Word documents masquerading as official documents from the Ministry of Human Resources and Social Security of China. Users are tricked into providing bank card details and passwords under the guise of identity verification and authentication processes.
QR Code-Based Phishing Campaign
QR code phishing attacks have escalated significantly this year, with cybercriminals leveraging this technology to steal personal and financial information. Threat actors (TAs) are embedding QR codes in office documents and redirecting users to fraudulent websites designed to harvest sensitive data.
In the ever-evolving cyber threat landscape, a new vector has emerged: QR code-based phishing campaign. Cybercriminals are increasingly embedding QR codes in malicious documents, which when scanned direct users to fraudulent websites. This tactic has seen a marked rise in 2024 following a trend that started during the COVID-19 pandemic, when QR codes became widely adopted for contactless transactions and information sharing.
The Hoxhunt Challenge highlighted a 22% increase in QR code phishing during late 2023, and research by Abnormal Security indicates that 89.3% of these attacks aim to steal credentials.
The growing familiarity with QR codes has created a false sense of security, making it easier for cybercriminals to exploit them. QR codes can mask destination URLs, preventing users from easily verifying the legitimacy of the site they are being redirected to.
Recent QR Code Campaigns and Techniques
Recently, Cyble Research and Intelligence Labs uncovered a sophisticated phishing campaign targeting individuals in China. This campaign saw the use of Microsoft Word documents embedded with QR codes, which are distributed via spam email attachments. The documents were designed to appear as official notices from the Ministry of Human Resources and Social Security of China, offering labor subsidies above 1000 RMB to lure victims.
[caption id="attachment_77666" align="aligncenter" width="769"] MS Word file containing QR code (Source: Cyble)[/caption]
The documents are meticulously crafted to look authentic, complete with official logos and language that mimics government communications. Once the QR code in the document is scanned, it redirects the user to a phishing site designed to collect sensitive information.
This particular campaign stands out due to its use of a Domain Generation Algorithm (DGA), which generates a series of seemingly random domain names. DGA is a program that generates large numbers of new domain names. Cybercriminals and botnet operators generally use it to frequently change the domains used to launch malware attacks. This technique enables hackers to avoid malware-detection solutions that block specific domain names and static IP addresses.
The latest campaign isn't an isolated incident. A similar phishing operation was documented in January 2023 by Fortinet, where cybercriminals impersonated another Chinese government agency. This resurgence in QR code phishing attacks indicates a persistent threat targeting Chinese citizens, with malicious actors continually refining their tactics to evade detection.
The QR Code Phishing Process
The phishing process begins with the user scanning the QR code from the malicious Word document. This action takes them to the phishing site, which initially displays a dialogue box promising a labor subsidy. The site is designed to appear official, complete with government logos and formal language to enhance credibility.
The phishing site instructs the user to provide personal information, starting with their name and national ID. This step is presented as a necessary part of the application process for the subsidy. Once the user enters this information, they are directed to a second page that requests detailed bank card information, including the card number, phone number, and balance. This information is ostensibly required for identity verification and to process the subsidy.
After collecting the bank card details, the phishing site asks the user to wait while their information is "verified." This waiting period is a tactic used to add a sense of legitimacy to the process. Following this, the site prompts the user to enter their bank card password, under the guise of further verification.
This password is suspected to be the same as the payment password used for domestic credit card transactions. By obtaining this password along with the card details, the threat actors can perform unauthorized transactions, leading to significant financial losses for the victim.
Phishing Activity Technical IoCs
The phishing activity begins when the user scans the QR code embedded in the Word document. This action directs them to the link βhxxp://wj[.]zhvsp[.]comβ. This initial URL then redirects to a subdomain, βtiozl[.]cnβ, created using a DGA. The use of a DGA means the phishing URLs are constantly changing, making them harder to block preemptively.
[caption id="attachment_77670" align="aligncenter" width="1024"] Landing page of phishing site (Source: Cyble)[/caption]
The domain βtiozl[.]cnβ is hosted on the IP address β20.2.161[.]134β. This IP address is associated with multiple other domains, suggesting a large-scale phishing operation. The domains linked to this campaign are:
- 2wxlrl.tiozl[.]cn
- op18bw[.]tiozl.cn
- gzha31.tiozl[.]cn
- i5xydb[.]tiozl.cn
- hzrz7c.zcyyl[.]com
Further investigation revealed that the SHA-256 fingerprint of an SSH server host key associated with the IP address β20.2.161[.]134β is linked to 18 other IPs, all within the same Autonomous System Number (ASN), AS8075, and located in Hong Kong. These IPs host URLs with similar patterns, indicating a coordinated effort to deploy numerous phishing sites.
The rise in QR code phishing attacks underscores the increasing sophistication and adaptability of cybercriminals. By exploiting the widespread use of QR codes - especially in a post-pandemic world - these attacks effectively lure users into divulging sensitive financial information.
The recent campaign targeting Chinese citizens highlights the severity of this threat, as malicious actors use seemingly official documents to gather card details and passwords, leading to significant financial losses. This trend emphasizes the need for heightened vigilance and robust security measures to protect against such evolving threats.
Recommendations for Mitigation
To mitigate the risk of QR code phishing attacks, CRIL said it is crucial to follow these cybersecurity best practices:
1. Scan QR codes from trusted sources only: Avoid scanning codes from unsolicited emails, messages, or documents, especially those offering financial incentives or urgent actions.
2. Verify URLs before proceeding: After scanning a QR code, carefully check the URL for legitimacy, such as official domains and secure connections (https://).
3. Install reputable antivirus and anti-phishing software: These tools can detect and block malicious websites and downloads.
4. Stay informed about phishing techniques: Educate yourself and others about the risks associated with QR codes to prevent successful phishing attacks.
5. Use two-factor authentication (2FA): This adds an extra layer of security, making it harder for attackers to gain unauthorized access.
6. Keep software up to date: Ensure your operating systems, browsers, and applications are updated with the latest security patches to protect against known vulnerabilities.
7. Use secure QR code scanner apps: Consider apps that check URLs against a database of known malicious sites before opening them.
8. Monitor financial statements regularly: Review your bank and credit card statements for unauthorized transactions and report any suspicious activity immediately.
βObesity firstβ doctors say they start with one medication, to treat obesity, and often find other chronic diseases, like rheumatoid arthritis, simply vanish.
With Wegovy, one of the new obesity drugs, Lesa Walton not only lost more than 50 pounds; her arthritis cleared up and she no longer needed pills to lower her blood pressure, she said.
In high-tech labs, workers are generating data to train A.I. algorithms to design better medicine, faster. But the transformation is just getting underway.
Chips in a container at Terray Therapeutics in Monrovia, Calif. Each of the custom-made chips has millions of minuscule wells for measuring drug screening reactions quickly and accurately.
While trying to save large amphibians native to Japan, herpetologists in the country unexpectedly found a way to potentially save an even bigger species in China.
But this isn't really about the software. It's about what software promises usβthat it will help us become who we want to be, living the lives we find most meaningful and fulfilling. The idea of research as leisure activity has stayed with me because it seems to describe a kind of intellectual inquiry that comes from idiosyncratic passion and interest. It's not about the formal credentials. It's fundamentally about play. It seems to describe a life where it's just fun to be reading, learning, writing, and collaborating on ideas. from research as leisure activity by Celine Nguyen [Personal Canon]
This post walks through the vulnerabilities we disclosed affecting Gradio, and our work with Hugging Face to harden the Spaces platform after a recently reported potential breach.
Firearms often are not stored safely in U.S. homes, a federal survey found. At the same time, gun-related suicides and injuries to children are on the rise.
A handgun kept in a portable case with biometric fingerprint access. Gun storage practices vary, but in a new survey about half of gun owners with loaded firearms at home did not lock them away.
Every year, the Eurovision Song Contest captivates millions of viewers across Europe and beyond, turning a simple music competition into a cultural phenomenon. This popularity extends to various forms of betting, with numerous gambling sites offering odds on Eurovision outcomes. Eurovision has grown from a small song competition into a massive international event, drawing in [β¦]
On June 2nd, 2024, Snowflake released a joint statement with Crowdstrike and Mandiant addressing reports of β[an] ongoing investigation involving a targeted threat campaign against some Snowflake customer accounts.β A SpecterOps customer contacted me about their organizationβs response to this campaign and mentioned that there seems to be very little security-based information related to Snowflake. In their initial statement, Snowflake recommended the following steps for organizations that may be affected (or that want to avoid being affected, for that matter!):
Enforce Multi-Factor Authentication on all accounts;
Set up Network Policy Rules to only allow authorized users or only allow traffic from trusted locations (VPN, Cloud workload NAT, etc.);Β and
Impacted organizations should reset and rotate Snowflake credentials.
While these recommendations are a good first step, I wondered if there was anything else we could do once we better grasped Snowflakeβs Access Control Model (and its associated Attack Paths) and better understood the details of the attackerβs activity on the compromised accounts. In this post, I will describe the high-level Snowflake Access Control Model, analyze the incident reporting released by Mandiant, and provide instructions on graphing the βaccess modelβ of your Snowflake deployment.
These recommendations address how organizations might address initial access to their Snowflake instance. However, I was curious about βpost-exploitationβ in a Snowflake environment. After a quick Google search, I realized there is very little threat research on Snowflake. My next thought was to check out Snowflakeβs access control model to better understand the access landscape. I hoped that if I could understand how users are granted access to resources in a Snowflake account, I could start to understand what attackers might do once they are authenticated. I also thought we could analyze the existing attack paths to make recommendations to reduce the blast radius of a breach of the type Crowdstrike and Mandiant reported.
While we have not yet integrated Snowflake into BloodHound Community Edition (BHCE) or Enterprise (BHE), we believe there is value in taking a graph-centric approach to analyzing your deployment, as it can help you understand the impact of a campaign similar to the one described in the intro to thisΒ post.
Snowflake Access ControlΒ Model
My first step was to search for any documentation on Snowflakeβs access control model. I was pleased to find a page providing a relatively comprehensive and simple-to-understand model description. They describe their model as a mix of Discretionary Access Control, where βeach object has an owner, who can in turn grant access to that object,β and Role-based Access Control, where βprivileges are assigned to roles, which are in turn assigned to users.β These relationships are shown in the imageΒ below:
Notice that Role 1 βownsβ Objects 1 and 2. Then, notice that two different privileges are granted from Object 1 to Role 2 and that Role 2 is granted to Users 1 and 2. Also, notice that Roles can be granted to other Roles, which means there is a nested hierarchy similar to groups in Active Directory. One thing that I found helpful was to flip the relationship of some of these βedges.β In this graphic, they are pointing toward the grant, but the direction of access is the opposite. Imagine that you are User 1, and you are granted Role 2, which has two Privileges on Object 1. Therefore, you have two Privileges on Object 1 through transitivity.
We have a general idea of how privileges on objects are granted, but what types of objects does Snowflake implement? They provide a graphic to show the relationship between these objects, which they describe as βhierarchical.β
Notice that at the top of the hierarchy, there is an organization. Each organization can have one or many accounts. For example, the trial I created to do this research has only one Account, but the client that contacted me has ~10. The Account is generally considered to be the nexus of everything. It is helpful to think of an Account as the equivalent of an Active Directory Domain. Within the Account are Users, Roles (Groups), Databases, Warehouses (virtual compute resources), and many other objects, such as Security Integrations. Within the Database context is a Schema, and within the Schema context are Tables, Views, Stages (temporary stores for loading/unloading data),Β etc.
As I began understanding the implications of each object and the types of privileges each affords, I started to build a model showing their possible relationships. In doing so, I found it helpful to start at the top of the hierarchy (the account) and work my way down with respect to integrating entity types into the model. This is useful because access to entities often depends on access to their parent. For example, a user can only interact with a schema if the user also has access to the schemaβs parent database. This allows us to abstract away details and make educated inferences about lower-level access. Below, I will describe the primary objects that I consider in myΒ model.
Account (thinkΒ Domain)
The account is the equivalent to the domain. All objects exist within the context of the account. When you log into Snowflake, you log in as a user within a specific account. Most administrative privileges are privileges to operate on the account, such as CREATE USER, MANAGE GRANTS, CREATE ROLE, CREATE DATABASE, EXECUTE TASK,Β etc.
Users (precisely what you think theyΒ are)
Users are your identity in the Snowflake ecosystem. When you log into the system, you do so as a particular user, and you have access to resources based on your granted roles and the roleβs granted privileges.
Roles (thinkΒ Groups)
Roles are the primary object to which privileges are assigned. Users can be granted βUSAGEβ of a role, similar to being added as group members. Roles can also be granted to other roles, which creates a nested structure that facilitates granular control of privileges. There are ~ five default admin accounts. The first is ACCOUNTADMIN, which is the Snowflake equivalent of Domain Admin. The remaining four are ORGADMIN, SYSADMIN, SECURITYADMIN, and USERADMIN.
A Warehouse is βa cluster of computer resourcesβ¦ such as CPU, memory, and temporary storageβ used to perform database-related operations in a Snowflake session. Operations such as retrieving rows from tables, updating rows in tables, and loading/unloading data from tables all require a warehouse.
A database is defined as βa logical grouping of schemas.β It is the container for information that we would expect attackers to target. While the database object itself does not contain any data, a user must have access to the database to access its subordinate objects (Schemas, Tables,Β etc.).
Privileges define who can perform which operation on which resources. In our context, privileges are primarily assigned to roles. Snowflake supports many privileges, some of which apply in a global or account context (e.g., CREATE USER), while others are specific to an object type (e.g., CREATE SCHEMA on a Database). Users accumulate privileges through the Roles that they have been granted recursively.
Access Graph
With this basic understanding of Snowflakeβs access control model, we can create a graph model that describes the relationships between entities via privileges. For instance, we know that a user can be granted the USAGE privilege of a role. This is the equivalent of an Active Directory user being a MemberOf a group. Additionally, we find that a role can be granted USAGE of another role, similar to group nesting in AD. Eventually, we can produce this relatively complete initial model for the Snowflake βaccessΒ graph.β
This model can help us better understand what likely happened during the incident. It can also help us better understand the access landscape of our Snowflake deployment, which can help us reduce the blast radius should an attacker gainΒ access.
About theΒ Incident
As more details have emerged, it has become clear that this campaign targeted customer credentials rather than Snowflakeβs production environment. Later, on June 10th, Mandiant released a more detailed report describing some of the threat groupβs activity discovered during the investigation.
Mandiant describes a typical scenario where threat actors compromise the computers of contractors that companies hire to build, manage, or administer their Snowflake deployment. In many cases, these contractors already have administrative privileges, so any compromise of their credentials can lead to detrimental effects. The existing administrative privileges indicate that the threat actor had no need to escalate privilege via an attack path or compromise alternative identities during this activity.
Mandiant describes the types of activity the attackers were observed to have implemented. They appear interested in enumerating database tables to find interesting information for exfiltration. An important observation is that, based on the reported activity, the compromised user seems to have admin or admin-adjacent privileges on the Snowflake account.
In this section, we will talk about each of these commands, what they do and how we can understand them in the context of ourΒ graph.
As Mandiant describes, the first command is a Discovery command meant to list all the tables available. According to the documentation, a user requires at least the USAGE privilege on the Schema object that contains the table to execute this command directly. It is common for a production Snowflake deployment to have many databases, each with many schemas, so access to tables will likely be limited to most non-admins. We can validate this in the graph,Β though!
Next, we see that they run the SELECT command. This indicates that they must have found one or more tables from the previous command that interested them. This command works similarly to the SQL query and returns the rows in the table. In this case, they are dumping the entire table. The privilege documentation states that a user must have the SELECT privilege on the specified table (<Target Table>) to execute this command. Additionally, the user must have the USAGE privilege on the parent database (<Target Database>) and schema (<Target Schema>).
Like tables, stages exist within the schema context; thus, the requisite privilege, CREATE STAGE, exists at the schema level (aka <Redacted Schema>). The user would also require the USAGE privilege on the database (<Redacted Database>). Therefore, a user can have the ability to create a stage for one schema but not another. In general, this is a privilege that can be granted to a limited set of individuals, especially when it comes to sensitive databases/schemas.
Finally, the attackers call the COPY INTO command, which is a way to extract data from the Snowflake database. Obviously, Mandiant redacted the path, but one possible example would be to use the temporary stage to copy the data to an Amazon S3 bucket. In this case, the attacker uses the COPY INTO <location> variant, which requires the WRITE privilege. Of course, the attacker created the stage resource in the previous command, so they would likely have OWNERSHIP of the stage, granting them full control of theΒ object.
At this point, some of you might be interested in checking out your Snowflake Access Graph. This section walks through how to gather the necessary Snowflake data, stand up Neo4j, and build the graph. It also provides some sample Cypher queries relevant to Snowflakeβs recommendations.
Collecting Data
The first step is to collect the graph-relevant data from Snowflake. The cool thing is that this is actually a relatively simple process. Iβve found that Snowflakeβs default web client, Snowsight, does a fine job gathering this information. You can navigate to Snowsight once youβve logged in by clicking on the Query data button at the top of the HomeΒ page.
Once there, you will have the opportunity to execute commands. This section will describe the commands that collect the data necessary to build the graph. My parsing script is built for CSV files that follow a specific naming convention. Once your command has returned results, click the download button (downward pointing arrow) and select the βDownload asΒ .csvβΒ option.
The model supports Accounts, Applications, Databases, Roles, Users, and Warehouses. This means we will have to query those entities, which will serve as the nodes in our graph. This will download the file with a name related to your account. My parsing script expects the output of certain commands to be named in a specific way. The expected name will be shared in the corresponding sectionsΒ below.
Iβve found that I can query Applications, Databases, Roles, and Users as an unprivileged user. However, this is different for Accounts, which require ORGADMIN, and Warehouses, which require instance-specific access (e.g., ACCOUNTADMIN).
Note: As mentioned above, users can only enumerate warehouses for which they have been granted privileges. One way to grant a non-ACCOUNTADMIN user visibility of all warehouses is to grant the MANAGE WAREHOUSESprivilege.
Accounts
At this point, we have almost all the entity data we need. We have one final query that will allow us to gather details about our Snowflake account. This query can only be done by the ORGADMIN role. Assuming your user has been granted ORGADMIN, go to the top right corner of the browser and click on your current role. This will result in a drop-down that displays all of the roles that are effectively granted to your user. Here, you will select ORGADMIN, allowing you to run commands in the context of the ORGADMINΒ role.
Once complete, run the following command to list the accountΒ details.
Finally, we must gather information on privilege grants. These are maintained in the ACCOUNT_USAGE schema of the default SNOWFLAKE database. By default, these views are only available to the ACCOUNTADMIN role. Still, users not granted USAGE of the ACCOUNTADMIN role can be granted the necessary read access via the SECURITY_VIEWER database role. The following command does this (if run as ACCOUNTADMIN):
GRANT DATABASE ROLE snowflake.SECURITY_VIEWER TO <Role>
Once you have the necessary privilege, you can query the relevant views and export them to a CSV file. The first view is grants_to_users, which maintains a list of which roles have been granted to which users. You can enumerate this list using the following command. Then save it to a CSV file and rename it grants_to_users.csv.
SELECT * FROM snowflake.account_usage.grants_to_users;
The final view is grants_to_roles, which maintains a list of all the privileges granted to roles. This glue ultimately allows users to interact with the different Snowflake entities. This view can be enumerated using the following command. The results should be saved as a CSV file named grants_to_roles.csv.
SELECT * FROM snowflake.account_usage.grants_to_roles WHERE GRANTED_ON IN ('ACCOUNT', 'APPLICATION', 'DATABASE', 'INTEGRATION', 'ROLE', 'USER', 'WAREHOUSE');
Setting upΒ Neo4j
At this point, we have a Cypher statement that we can use to generate the Snowflake graph, but before we can do that, we need a Neo4j instance. The easiest way that I know of to do this is to use the BloodHound Community Edition docker-compose deployment option.
Note: While we wonβt use BHCE specifically in this demo, the overarching docker-compose setup includes a Neo4j instance configured to support thisΒ example.
To do this, you must first install Docker on your machine. Once complete, download this example docker-compose yaml file I derived from the BHCE GitHub repository. Next, open docker-compose.yaml in a text editor and edit Line 51 to point to the folder on your host machine (e.g., /Users/jared/snowflake:/var/lib/neo4j/import/) where you wrote the Snowflake data files (e.g., grants_to_roles.csv). This will create a bind mount between your host and the container. You are now ready to start the container by executing the following command:
docker-compose -f /path/to/docker-composer.yaml up -d
This will cause Docker to download and run the relevant Docker containers. For this Snowflake graph, we will interact directly with Neo4j as this model has not been integrated into BloodHound. You can access the Neo4j web interface by browsing to 127.0.0.1:7474 and logging in using the default credentials (neo4j:bloodhoundcommunityedition).
Data Ingest
Once youβve authenticated to Neo4j, it is time for data ingest. I originally wrote a PowerShell script that would parse the CSV files and handcraft Cypher queries to create the corresponding nodes and edges, but SadProcessor showed me a better way to approach ingestion. He suggested using the LOAD CSV clause. According to Neo4j, βLOAD CSV is used to import data from CSV files into a Neo4j database.β This dramatically simplifies ingesting your Snowflake data AND is much more efficient than my initial PowerShell script. This section describes the Cypher queries that I use to import Snowflake data. Before you begin, knowing that each command must be run individually is essential. Additionally, these commands assume that youβve named your files as suggested. Therefore, the file listing of the folder you specified in the Docker Volume (e.g., /Users/jared/snowflake) should lookΒ this:
-rwx------@ 1 cobbler staff 677 Jun 12 20:17 account.csv -rwx------@ 1 cobbler staff 227 Jun 12 20:17 application.csv -rwx------@ 1 cobbler staff 409 Jun 12 20:17 database.csv -rwx------@ 1 cobbler staff 8362 Jun 12 20:17 grants_to_roles.csv -rwx------@ 1 cobbler staff 344 Jun 12 20:17 grants_to_users.csv -rwx------@ 1 cobbler staff 114 Jun 12 20:17 integration.csv -rwx------@ 1 cobbler staff 895 Jun 12 20:17 role.csv -rwx------@ 1 cobbler staff 12350 Jun 12 20:17 table.csv -rwx------@ 1 cobbler staff 917 Jun 12 20:17 user.csv -rwx------@ 1 cobbler staff 436 Jun 12 20:17 warehouse.csv
Note: If you donβt have a Snowflake environment, but still want to check out the graph, you can use my sample data set by replacing file:/// with https://gist.githubusercontent.com/jaredcatkinson/c5e560f7d3d0003d6e446da534a89e79/raw/c9288f20e606d236e3775b11ac60a29875b72dbc/ in eachΒ query.
Ingest Accounts
LOAD CSV WITH HEADERS FROM 'file:///account.csv' AS line CREATE (:Account {name: line.account_locator, created_on: line.created_on, organization_name: line.organization_name, account_name: line.account_name, snowflake_region: line.snowflake_region, account_url: line.account_url, account_locator: line.account_locator, account_locator_url: line.account_locator_url})
Ingest Applications
LOAD CSV WITH HEADERS FROM 'file:///application.csv' AS line CREATE (:Application {name: line.name, created_on: line.created_on, source_type: line.source_type, source: line.source})
Ingest Databases
LOAD CSV WITH HEADERS FROM 'file:///database.csv' AS line CREATE (:Database {name: line.name, created_on: line.created_on, retention_time: line.retention_time, kind: line.kind})
Ingest Integrations
LOAD CSV WITH HEADERS FROM 'file:///integration.csv' AS line CREATE (:Integration {name: line.name, created_on: line.created_on, type: line.type, category: line.category, enabled: line.enabled})
Ingest Roles
LOAD CSV WITH HEADERS FROM 'file:///role.csv' AS line CREATE (:Role {name: line.name, created_on: line.created_on, assigned_to_users: line.assigned_to_users, granted_to_roles: line.granted_to_roles})
Ingest Users
LOAD CSV WITH HEADERS FROM 'file:///user.csv' AS line CREATE (:User {name: line.name, created_on: line.created_on, login_name: line.login_name, first_name: line.first_name, last_name: line.last_name, email: line.email, disabled: line.disabled, ext_authn_duo: line.ext_authn_duo, last_success_login: line.last_success_login, has_password: line.has_password, has_rsa_public_key: line.has_rsa_public_key})
Ingest Warehouses
LOAD CSV WITH HEADERS FROM 'file:///warehouse.csv' AS line CREATE (:Warehouse {name: line.name, created_on: line.created_on, state: line.state, size: line.size})
Ingest Grants toΒ Users
LOAD CSV WITH HEADERS FROM 'file:///grants_to_users.csv' AS usergrant CALL { WITH usergrant MATCH (u:User) WHERE u.name = usergrant.GRANTEE_NAME MATCH (r:Role) WHERE r.name = usergrant.ROLE MERGE (u)-[:USAGE]->(r) }
Ingest Grants toΒ Roles
:auto LOAD CSV WITH HEADERS FROM 'file:///grants_to_roles.csv' AS grant CALL { WITH grant MATCH (src) WHERE grant.GRANTED_TO = toUpper(labels(src)[0]) AND src.name = grant.GRANTEE_NAME MATCH (dst) WHERE grant.GRANTED_ON = toUpper(labels(dst)[0]) AND dst.name = grant.NAME FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'USAGE' THEN [1] ELSE [] END | MERGE (src)-[:USAGE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'OWNERSHIP' THEN [1] ELSE [] END | MERGE (src)-[:OWNERSHIP]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLYBUDGET' THEN [1] ELSE [] END | MERGE (src)-[:APPLYBUDGET]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'AUDIT' THEN [1] ELSE [] END | MERGE (src)-[:AUDIT]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MODIFY' THEN [1] ELSE [] END | MERGE (src)-[:MODIFY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MONITOR' THEN [1] ELSE [] END | MERGE (src)-[:MONITOR]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'OPERATE' THEN [1] ELSE [] END | MERGE (src)-[:OPERATE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY AGGREGATION POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_AGGREGATION_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY AUTHENTICATION POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_AUTHENTICATION_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY MASKING POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_MASKING_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY PACKAGES POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_PACKAGES_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY PASSWORD POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_PASSWORD_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY PROTECTION POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_PROTECTION_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY ROW ACCESS POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_ROW_ACCESS_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'APPLY SESSION POLICY' THEN [1] ELSE [] END | MERGE (src)-[:APPLY_SESSION_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'ATTACH POLICY' THEN [1] ELSE [] END | MERGE (src)-[:ATTACH_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'BIND SERVICE ENDPOINT' THEN [1] ELSE [] END | MERGE (src)-[:BIND_SERVICE_ENDPOINT]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CANCEL QUERY' THEN [1] ELSE [] END | MERGE (src)-[:CANCEL_QUERY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE ACCOUNT' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_ACCOUNT]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE API INTEGRATION' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_API_INTEGRATION]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE APPLICATION' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_APPLICATION]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE APPLICATION PACKAGE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_APPLICATION_PACKAGE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE COMPUTE POOL' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_COMPUTE_POOL]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE CREDENTIAL' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_CREDENTIAL]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE DATA EXCHANGE LISTING' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_DATA_EXCHANGE_LISTING]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE DATABASE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_DATABASE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE DATABASE ROLE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_DATABASE_ROLE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE EXTERNAL VOLUME' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_EXTERNAL_VOLUME]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE INTEGRATION' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_INTEGRATION]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE NETWORK POLICY' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_NETWORK_POLICY]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE REPLICATION GROUP' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_REPLICATION_GROUP]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE ROLE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_ROLE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE SCHEMA' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_SCHEMA]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE SHARE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_SHARE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE USER' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_USER]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'CREATE WAREHOUSE' THEN [1] ELSE [] END | MERGE (src)-[:CREATE_WAREHOUSE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'EXECUTE DATA METRIC FUNCTION' THEN [1] ELSE [] END | MERGE (src)-[:EXECUTE_DATA_METRIC_FUNCTION]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'EXECUTE MANAGED ALERT' THEN [1] ELSE [] END | MERGE (src)-[:EXECUTE_MANAGED_ALERT]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'EXECUTE MANAGED TASK' THEN [1] ELSE [] END | MERGE (src)-[:EXECUTE_MANAGED_TASK]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'EXECUTE TASK' THEN [1] ELSE [] END | MERGE (src)-[:EXECUTE_TASK]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'IMPORT SHARE' THEN [1] ELSE [] END | MERGE (src)-[:IMPORT_SHARE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MANAGE GRANTS' THEN [1] ELSE [] END | MERGE (src)-[:MANAGE_GRANTS]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MANAGE WAREHOUSES' THEN [1] ELSE [] END | MERGE (src)-[:MANAGE_WAREHOUSES]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MANAGEMENT SHARING' THEN [1] ELSE [] END | MERGE (src)-[:MANAGEMENT_SHARING]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'MONITOR EXECUTION' THEN [1] ELSE [] END | MERGE (src)-[:MONITOR_EXECUTION]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'OVERRIDE SHARE RESTRICTIONS' THEN [1] ELSE [] END | MERGE (src)-[:OVERRIDE_SHARE_RESTRICTIONS]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'PURCHASE DATA EXCHANGE LISTING' THEN [1] ELSE [] END | MERGE (src)-[:PURCHASE_DATA_EXCHANGE_LISTING]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'REFERENCE USAGE' THEN [1] ELSE [] END | MERGE (src)-[:REFERENCE_USAGE]->(dst)) FOREACH (_ IN CASE WHEN grant.PRIVILEGE = 'USE ANY ROLE' THEN [1] ELSE [] END | MERGE (src)-[:USE_ANY_ROLE]->(dst)) } IN TRANSACTIONS
Once you finish executing these commands you can validate that the data is in the graph by running a query. The query below returns any entity with a path to the Snowflake account.
MATCH p=()-[*1..]->(a:Account) RETURN p
This is a common way to find admin users. While Snowflake has a few default admin Roles, such as ACCOUNTADMIN, ORGADMIN, SECURITYADMIN, SYSADMIN, and USERADMIN, granting administrative privileges to custom roles is possible.
Queries
Having a graph is great! However, the value is all about the questions you can ask. Iβve only been playing around with this Snowflake graph for a few days. Still, I created a few queries that will hopefully help you gather context around the activity reported in Mandiantβs report and your compliance with Snowflakeβs recommendations.
Admins withoutΒ MFA
Snowflakeβs primary recommendation to reduce your exposure to this campaign and others like it is to enable MFA on all accounts. While achieving 100% coverage on all accounts may take some time, they also recommend enabling MFA on users who have been granted the ACCOUNTADMIN Role. Based on my reading of the reporting, the attackers likely compromised the credentials of admin users, so it seems reasonable to start with these highly privileged accountsΒ first.
There are two approaches to determining which users have admin privileges. The first is to assume that admins will be granted one of the default admins roles, as shownΒ below:
MATCH p=((n:User WHERE n.ext_authn_duo = "false")-[:USAGE*1..]->(r:Role WHERE r.name CONTAINS "ADMIN")) RETURN p
Here, we see seven users who have been granted USAGE of a role with the string βADMINβ in its name. While this is a good start, the string βADMINβ does not necessarily mean that the role has administrative privileges, and its absence does not mean that the role does not have administrative privileges. Instead, I recommend searching for admins based on their effective privileges.
This second query considers that admin privileges can be granted to custom roles. For example, the MANAGE_GRANTS privilege, shown below, βgrants the ability to grant or revoke privileges on any object as if the invoking role were the owner of the object.β This means that if a user has this privilege, they can grant themselves or anyone access to any object theyΒ want.
MATCH p=((n:User WHERE n.ext_authn_duo = "false")-[:USAGE*1..]->(r:Role)-[:MANAGE_GRANTS]->(a:Account)) RETURN p
Here, we see five users not registered for MFA who have MANAGE_GRANTS over the Snowflake Account. Two users are granted USAGE of the ACCOUNTADMINS role, and the other three are granted USAGE of a custom role. Both ACCOUNTADMINS and the custom role are granted USAGE of the SECURITYADMINS role, which is granted MANAGE_GRANTS on theΒ account.
Restated in familiar terms, two users are members of the ACCOUNTADMINS group, which is nested inside the SECURITYADMINS group, which has SetDACL right on the DomainΒ Head.
User Access to aΒ Database
According to Mandiant, most of the attackerβs actions focused on data contained within database tables. While my graph does not currently support schema or table entities, it is important to point out that the documentation states that βoperating on a table also requires the USAGE privilege on the parent database and schema.β This means that we can use the graph to understand which users have access to which database and then infer that they likely have access to the schema and tables within the database.
MATCH p=((u:User)-[:USAGE*1..]->(r:Role)-[:OWNERSHIP]->(d:Database WHERE d.name = "<DATABASE NAME GOES HERE>")) RETURN p
Here, the Jared and SNOWFLAKE users have OWNERSHIP of the SNOWFLAKE_SAMPLE_DATA database via the ACCOUNTADMIN role.
This query shows all users that have access to a specified databases. If you would like to check access to all databases you can run thisΒ query:
MATCH p=((u:User)-[:USAGE*1..]->(r:Role)-[]->(d:Database)) RETURN p
Stale UserΒ Accounts
Another simple example is identifying users that have never been used (logged in to). Pruning unused users might reduce the overall attack surfaceΒ area.
MATCH (n:User WHERE n.last_success_login = "") RETURN n
Conclusion
I hope you found this overview and will find this graph capability useful. Iβm looking forward to your feedback regarding the graph! If you write a useful query, please share it, and I will put it in the post with credit. Additionally, if you think of extending the graph, please let me know, and Iβll do my best to facilitate it.
Before I go, I want to comment on Snowflakeβs recommendations in the aftermath of this campaign. As I mentioned, Snowflakeβs primary recommendation is to enable MFA on all accounts. It is worth mentioning, in their defense, that Snowflake has always (at least since before this incident) recommended that MFA be enabled on any user granted the ACCOUNTADMIN role (the equivalent of DomainΒ Admin).
That being said, the nature of web-based platforms means that if an attacker compromises a system with a Snowflake session, they likely can steal the session token and reuse it even if the user has MFA enabled. Austin Baker, who goes by @BakedSec on Twitter, pointed thisΒ out.
This indicates that we must look beyond how we stop attackers from getting access. We must understand the access landscape within our information systems. Ask yourself, βCan you answer which users can use the DATASCIENCE Database in your Snowflake deployment?β With this graph, that question is trivial to answer, but without one, we find that most organizations cannot answer these questions accurately. When nested groups (roles in this case) are involved, it is very easy for there to be a divergence between intended access and effective access. This only gets worse over time. I think of it asΒ entropy.
We must use a similar approach for cloud accounts as on-prem administration. You donβt browse the web with your Domain Administrator account. No, you have two accounts, one for administration and one for day-to-day usage. You might even have a system that is dedicated to administrative tasks. These same ideas should apply to cloud solutions like Snowflake. Are you analyzing the data in a table? Great, use your Database Reader account. Now you need to grant Luke a role so he can access a warehouse? Okay, hop on your Privileged Access Workstation and use your SECURITYADMIN account. The same Tier 0 concept applies in this context. I look forward to hearing your feedback!
UPDATE: Luke Jennings from Push Security added a new technique to the SaaS Attack Matrix called Session Cookie Theft. This technique shows one way that attackers, specifically if they have access to the SaaS userβs workstation, can steal relevant browser cookies in order to bypass MFA. This does not mean that organizations should not strive to enable MFA on their users, especially admin accounts, however it does demonstrate the importance of reducing attack paths within the SaaS applicationβs access control model. One way to think of it is that MFA is meant to make it more difficult for attackers to get in, but once theyβre in it is all about Attack Paths. The graph approach I demonstrate in this post is the first step to getting a handle of these Attack Paths to reduce the blast radius of a compromise.
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