Attack Chain

1. The attacker identifies a vulnerable RHEL system running Fast Datapath exposed to the network.
2. The attacker crafts a malicious network packet designed to exploit a memory corruption vulnerability within Fast Datapath.
3. The malicious packet is sent to the target system over the network.
4. Fast Datapath processes the packet, triggering a buffer overflow or other memory corruption error.
5. The memory corruption causes the Fast Datapath process to crash, leading to a denial-of-service condition.
6. (Alternative) The attacker exploits a separate vulnerability to read sensitive information from Fast Datapath’s memory.
7. The attacker exfiltrates the disclosed information.

Impact

Successful exploitation of these vulnerabilities could result in a denial of service, disrupting critical services and impacting business operations. The disclosure of sensitive information could also lead to further compromise, including unauthorized access to systems or data. The number of affected systems will depend on the prevalence of Fast Datapath deployments within RHEL environments.

Recommendation

• Deploy the Sigma rule Detect Suspicious Network Traffic to Fast Datapath to identify potential exploitation attempts (see below).
• Investigate and patch systems running Red Hat Enterprise Linux with Fast Datapath enabled as soon as patches are available from Red Hat.
• Monitor network traffic for anomalous patterns that may indicate attempts to exploit these vulnerabilities.

Attack Chain

1. Initial Compromise (via unconfirmed vector): An attacker identifies a vulnerable Red Hat Linux system running an affected kernel version. While the exact exploit vector isn’t specified in the advisory, it involves a vulnerability in the kernel.
2. Exploit Trigger: The attacker triggers a specific kernel vulnerability, such as those identified as CVE-2026-23001 or CVE-2026-31402, by sending a crafted input to a vulnerable kernel component. The specific method depends on the nature of each CVE.
3. Code Execution: Upon successful exploitation, the attacker achieves arbitrary code execution within the kernel context. This allows the attacker to run malicious code directly on the system.
4. Privilege Escalation: Leveraging the code execution capability, the attacker exploits another vulnerability (e.g., CVE-2025-68741) to escalate privileges to root or SYSTEM. This may involve exploiting race conditions, memory corruption bugs, or other privilege escalation flaws within the kernel.
5. System Control: With elevated privileges, the attacker gains full control over the compromised system. They can now access sensitive data, modify system configurations, install backdoors, or move laterally to other systems within the network.
6. Lateral Movement (Optional): The attacker uses the compromised system as a launching point to attack other systems on the network, potentially exploiting other vulnerabilities or using stolen credentials.
7. Persistence (Optional): The attacker establishes persistence on the compromised system to maintain access even after reboots. This may involve installing rootkits, modifying system startup scripts, or creating rogue user accounts.
8. Denial of Service/Data Exfiltration/etc.: Depending on their objectives, the attacker may use the compromised system to launch denial-of-service attacks against other targets, exfiltrate sensitive data, or cause other damage.

Impact

Successful exploitation of these kernel vulnerabilities can lead to complete system compromise, allowing attackers to execute arbitrary code, escalate privileges, and cause denial of service. The wide range of affected Red Hat Enterprise Linux and CodeReady Linux Builder versions implies a potentially large number of vulnerable systems. This can result in significant data breaches, system downtime, financial losses, and reputational damage for affected organizations.

Recommendation

• Apply the patches provided in Red Hat Security Advisories RHSA-2026:10756, RHSA-2026:10996, and RHSA-2026:11313 to remediate the vulnerabilities.
• Prioritize patching systems based on their criticality and exposure to external networks.
• Monitor systems for suspicious activity that may indicate exploitation attempts, focusing on unexpected kernel module loads or privilege escalations using process_creation logging.
• Deploy the Sigma rule detecting suspicious kernel module loading to identify potential rootkit installation attempts.
• Investigate any alerts generated by the deployed Sigma rules to determine the scope and impact of potential compromises.

Attack Chain

1. Attacker identifies a vulnerable Red Hat Hardened Images RPM (jq or pyOpenSSL) running on a target system.
2. Attacker crafts a malicious payload tailored to exploit a specific vulnerability within the identified RPM.
3. The attacker leverages a network connection to send the malicious payload to the target system.
4. The vulnerable RPM processes the payload, triggering the vulnerability (e.g., buffer overflow, arbitrary code injection).
5. The attacker gains unauthorized access to the system with the privileges of the compromised process.
6. The attacker escalates privileges to gain root access, potentially by exploiting further vulnerabilities or misconfigurations.
7. The attacker installs malware or modifies system files to establish persistence.
8. The attacker performs malicious activities, such as data exfiltration, denial-of-service attacks, or further lateral movement within the network.

Impact

Successful exploitation of these vulnerabilities in Red Hat Hardened Images RPMs could result in significant damage. An attacker could gain complete control over the affected systems, leading to data breaches, system outages, and further compromise of the network. The lack of specific vulnerability details makes quantifying the scope of impact difficult, but the potential for code execution makes this a high-priority threat. Affected sectors are broad due to the widespread use of Red Hat systems.

Recommendation

• Deploy the Sigma rule Detect Vulnerable Red Hat Package Installation to identify systems installing or upgrading the jq or pyOpenSSL packages, which may indicate a vulnerable system.
• Investigate systems identified by the Sigma rule for unusual network activity or suspicious processes to find potentially compromised hosts.
• Monitor process creation events for unexpected execution of binaries by the jq or pyOpenSSL processes to detect potential exploitation using the Detect Suspicious Process Execution by Vulnerable RPM Sigma rule.

Attack Chain

1. The attacker identifies a vulnerable endpoint or component within the Red Hat Ansible Automation Platform accessible remotely.
2. The attacker exploits a vulnerability, such as a flaw in input validation, to inject malicious code or scripts.
3. The attacker leverages the initial exploit to achieve arbitrary code execution on the target system.
4. The attacker escalates privileges to gain control over the Ansible Automation Platform instance.
5. The attacker uses the compromised platform to manipulate automation workflows and configurations.
6. The attacker deploys malicious playbooks to managed hosts, leading to further compromise.
7. The attacker exfiltrates sensitive data from the compromised hosts or the Ansible Automation Platform database.
8. The attacker launches denial-of-service attacks against critical infrastructure components, disrupting operations.

Impact

Successful exploitation of these vulnerabilities could have severe consequences. A denial-of-service attack could disrupt critical automation processes, leading to significant operational downtime. Arbitrary code execution could allow an attacker to gain complete control over the Ansible Automation Platform and managed hosts. Data manipulation could compromise the integrity of critical systems and data. Information disclosure could expose sensitive credentials and internal data. Cross-site scripting could be used to target administrators and users of the platform. The lack of specific victimology makes it difficult to estimate the number of potential victims, but the widespread use of Ansible suggests that a successful exploit could have a broad impact across numerous sectors.

Recommendation

• Review Red Hat security advisories related to Ansible Automation Platform and apply the necessary patches immediately to remediate potential vulnerabilities as they become available.
• Implement strong input validation and output encoding to prevent code injection and cross-site scripting attacks.
• Monitor network traffic for suspicious activity indicative of exploitation attempts, focusing on requests targeting the Ansible Automation Platform web interface.
• Deploy the Sigma rules provided below to detect potential exploitation attempts and malicious activity on the Ansible Automation Platform server (see rules section).
• Review and harden the security configuration of the Ansible Automation Platform to minimize the attack surface.
• Implement strict access controls to limit the exposure of sensitive data and functionality.

Attack Chain

1. The attacker identifies a vulnerable Red Hat Undertow instance exposed to the internet.
2. The attacker sends a specially crafted HTTP request designed to exploit one of the undisclosed vulnerabilities.
3. The vulnerable Undertow instance processes the malicious request, leading to a security bypass.
4. The attacker exploits the bypassed security measure to manipulate data within the application.
5. The attacker leverages another vulnerability to gain unauthorized access to sensitive information stored within the application or backend systems.
6. The attacker exfiltrates the compromised data or uses it to further compromise the system.
7. The attacker maintains persistence by creating backdoors.

Impact

Successful exploitation of these vulnerabilities could lead to significant data breaches, unauthorized modification of critical application data, and complete compromise of the affected system. The lack of specific vulnerability details makes it difficult to quantify the exact number of potential victims or targeted sectors. The impact ranges from data theft and service disruption to complete system takeover, depending on the specific vulnerabilities exploited and the application’s role.

Recommendation

• Monitor web server logs (category: webserver, product: linux) for suspicious HTTP requests, particularly those with unusual URI patterns or excessive length, using the provided Sigma rule.
• Implement rate limiting and input validation on all Undertow deployments to mitigate potential exploitation attempts.
• Review access control configurations for all applications using Undertow to ensure least privilege principles are enforced.

Attack Chain

While the exact nature of the vulnerabilities isn’t specified, we can infer a likely attack chain based on the reported impacts:

1. Initial Access: The attacker gains remote access to the Red Hat Developer Hub, either anonymously or using compromised credentials.
2. Vulnerability Identification: The attacker identifies a specific vulnerability to exploit, such as an injection flaw or a deserialization issue.
3. Exploit Delivery: The attacker crafts a malicious payload designed to exploit the identified vulnerability, delivering it via HTTP requests.
4. Code Execution: The exploited vulnerability allows the attacker to execute arbitrary code on the server hosting the Red Hat Developer Hub.
5. Privilege Escalation (Optional): The attacker may attempt to escalate privileges within the system to gain broader control.
6. Data Manipulation: Using the compromised system, the attacker modifies or exfiltrates sensitive data stored within the Red Hat Developer Hub.
7. Security Bypass: The attacker leverages vulnerabilities to bypass authentication or authorization mechanisms.
8. Denial of Service: The attacker floods the Red Hat Developer Hub with malicious requests, causing it to become unresponsive and unavailable to legitimate users.

Impact

Successful exploitation of these vulnerabilities could have severe consequences, including complete compromise of the Red Hat Developer Hub instance. An attacker could gain unauthorized access to sensitive data, disrupt services through denial-of-service attacks, and potentially pivot to other systems within the network. The lack of specific details about the affected versions and number of victims makes it challenging to quantify the full scope of the potential impact.

Recommendation

• Implement a web application firewall (WAF) rule to detect and block suspicious HTTP requests targeting Red Hat Developer Hub to mitigate exploit attempts (webserver log source).
• Monitor web server logs for unusual activity, such as unexpected HTTP status codes or large numbers of requests from a single IP address, to identify potential denial-of-service attacks (webserver log source).
• Deploy the Sigma rules provided below to your SIEM to detect potential exploitation attempts.