5.5 CVE-2022-1325

DOS Patch Exploit

A flaw was found in Clmg, where with the help of a maliciously crafted pandore or bmp file with modified dx and dy header field values it is possible to trick the application into allocating huge buffer sizes like 64 Gigabyte upon reading the file from disk or from a virtual buffer.
https://nvd.nist.gov/vuln/detail/CVE-2022-1325

Categories

CWE-400 : Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource. Certain automated dynamic analysis techniques may be effective in spotting resource exhaustion problems, especially with resources such as processes, memory, and connections. The technique may involve generating a large number of requests to the product within a short time frame. While fuzzing is typically geared toward finding low-level implementation bugs, it can inadvertently find resource exhaustion problems. This can occur when the fuzzer generates a large number of test cases but does not restart the targeted product in between test cases. If an individual test case produces a crash, but it does not do so reliably, then an inability to handle resource exhaustion may be the cause. Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold. Ensure that protocols have specific limits of scale placed on them. Ensure that all failures in resource allocation place the system into a safe posture. Chain: Python library does not limit the resources used to process images that specify a very large number of bands (CWE-1284), leading to excessive memory consumption (CWE-789) or an integer overflow (CWE-190). Go-based workload orchestrator does not limit resource usage with unauthenticated connections, allowing a DoS by flooding the service Resource exhaustion in distributed OS because of "insufficient" IGMP queue management, as exploited in the wild per CISA KEV. Product allows attackers to cause a crash via a large number of connections. Malformed request triggers uncontrolled recursion, leading to stack exhaustion. Chain: memory leak (CWE-404) leads to resource exhaustion. Driver does not use a maximum width when invoking sscanf style functions, causing stack consumption. Large integer value for a length property in an object causes a large amount of memory allocation. Web application firewall consumes excessive memory when an HTTP request contains a large Content-Length value but no POST data. Product allows exhaustion of file descriptors when processing a large number of TCP packets. Communication product allows memory consumption with a large number of SIP requests, which cause many sessions to be created. TCP implementation allows attackers to consume CPU and prevent new connections using a TCP SYN flood attack. Port scan triggers CPU consumption with processes that attempt to read data from closed sockets. Product allows attackers to cause a denial of service via a large number of directives, each of which opens a separate window. Product allows resource exhaustion via a large number of calls that do not complete a 3-way handshake. Mail server does not properly handle deeply nested multipart MIME messages, leading to stack exhaustion. Chain: anti-virus product encounters a malformed file but returns from a function without closing a file descriptor (CWE-775) leading to file descriptor consumption (CWE-400) and failed scans.

CWE-770 : Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any restrictions on the size or number of resources that can be allocated, in violation of the intended security policy for that actor. Manual static analysis can be useful for finding this weakness, but it might not achieve desired code coverage within limited time constraints. If denial-of-service is not considered a significant risk, or if there is strong emphasis on consequences such as code execution, then manual analysis may not focus on this weakness at all. Certain automated dynamic analysis techniques may be effective in producing side effects of uncontrolled resource allocation problems, especially with resources such as processes, memory, and connections. The technique may involve generating a large number of requests to the product within a short time frame. Manual analysis is likely required to interpret the results. Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits. Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410. Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold. For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server. Ensure that protocols have specific limits of scale placed on them. Chain: Python library does not limit the resources used to process images that specify a very large number of bands (CWE-1284), leading to excessive memory consumption (CWE-789) or an integer overflow (CWE-190). Language interpreter does not restrict the number of temporary files being created when handling a MIME request with a large number of parts.. Driver does not use a maximum width when invoking sscanf style functions, causing stack consumption. Large integer value for a length property in an object causes a large amount of memory allocation. Product allows exhaustion of file descriptors when processing a large number of TCP packets. Communication product allows memory consumption with a large number of SIP requests, which cause many sessions to be created. Product allows attackers to cause a denial of service via a large number of directives, each of which opens a separate window. CMS does not restrict the number of searches that can occur simultaneously, leading to resource exhaustion. web application scanner attempts to read an excessively large file created by a user, causing process termination Go-based workload orchestrator does not limit resource usage with unauthenticated connections, allowing a DoS by flooding the service

References

af854a3a-2127-422b-91ae-364da2661108 Patch Exploit

https://access.redhat.com/security/cve/CVE-2022-1325 Broken Link
https://bugzilla.redhat.com/show_bug.cgi?id=2074549 Exploit Issue Tracking Patch Third Party Advisory
https://github.com/GreycLab/CImg/commit/619cb58dd90b4e03ac68286c70ed98acbefd1c90 Patch Third Party Advisory
https://github.com/GreycLab/CImg/issues/343 Exploit Issue Tracking Third Party Advisory
https://github.com/GreycLab/CImg/pull/348 Patch Third Party Advisory
https://huntr.dev/bounties/a5e4fc45-8f14-4dd1-811b-740fc50c95d2/ Exploit Third Party Advisory

secalert@redhat.com Patch Exploit

https://access.redhat.com/security/cve/CVE-2022-1325 Broken Link
https://bugzilla.redhat.com/show_bug.cgi?id=2074549 Exploit Issue Tracking Patch Third Party Advisory
https://github.com/GreycLab/CImg/commit/619cb58dd90b4e03ac68286c70ed98acbefd1c90 Patch Third Party Advisory
https://github.com/GreycLab/CImg/issues/343 Exploit Issue Tracking Third Party Advisory
https://github.com/GreycLab/CImg/pull/348 Patch Third Party Advisory
https://huntr.dev/bounties/a5e4fc45-8f14-4dd1-811b-740fc50c95d2/ Exploit Third Party Advisory

CPE

cpe	start	end
Configuration 1
cpe:2.3:a:cimg:cimg::::::::		< 3.1.0

REMEDIATION

Patch

Url
https://bugzilla.redhat.com/show_bug.cgi?id=2074549
https://github.com/GreycLab/CImg/commit/619cb58dd90b4e03ac68286c70ed98acbefd1c90
https://github.com/GreycLab/CImg/pull/348
https://bugzilla.redhat.com/show_bug.cgi?id=2074549
https://github.com/GreycLab/CImg/commit/619cb58dd90b4e03ac68286c70ed98acbefd1c90
https://github.com/GreycLab/CImg/pull/348

EXPLOITS

Exploit-db.com

id	description	date
No known exploits

POC Github

Url
No known exploits

Other Nist (github, ...)

Url
https://bugzilla.redhat.com/show_bug.cgi?id=2074549
https://github.com/GreycLab/CImg/issues/343
https://huntr.dev/bounties/a5e4fc45-8f14-4dd1-811b-740fc50c95d2/
https://bugzilla.redhat.com/show_bug.cgi?id=2074549
https://github.com/GreycLab/CImg/issues/343
https://huntr.dev/bounties/a5e4fc45-8f14-4dd1-811b-740fc50c95d2/

CAPEC

Common Attack Pattern Enumerations and Classifications

id	description	severity
147	XML Ping of the Death An attacker initiates a resource depletion attack where a large number of small XML messages are delivered at a sufficiently rapid rate to cause a denial of service or crash of the target. Transactions such as repetitive SOAP transactions can deplete resources faster than a simple flooding attack because of the additional resources used by the SOAP protocol and the resources necessary to process SOAP messages. The transactions used are immaterial as long as they cause resource utilization on the target. In other words, this is a normal flooding attack augmented by using messages that will require extra processing on the target. [Survey the target] Using a browser or an automated tool, an attacker records all instance of web services to process XML requests. [Launch a resource depletion attack] The attacker delivers a large number of small XML messages to the target URLs found in the explore phase at a sufficiently rapid rate. It causes denial of service to the target application.	Medium
227	Sustained Client Engagement An adversary attempts to deny legitimate users access to a resource by continually engaging a specific resource in an attempt to keep the resource tied up as long as possible. The adversary's primary goal is not to crash or flood the target, which would alert defenders; rather it is to repeatedly perform actions or abuse algorithmic flaws such that a given resource is tied up and not available to a legitimate user. By carefully crafting a requests that keep the resource engaged through what is seemingly benign requests, legitimate users are limited or completely denied access to the resource.
492	Regular Expression Exponential Blowup An adversary may execute an attack on a program that uses a poor Regular Expression(Regex) implementation by choosing input that results in an extreme situation for the Regex. A typical extreme situation operates at exponential time compared to the input size. This is due to most implementations using a Nondeterministic Finite Automaton(NFA) state machine to be built by the Regex algorithm since NFA allows backtracking and thus more complex regular expressions.
125	Flooding An adversary consumes the resources of a target by rapidly engaging in a large number of interactions with the target. This type of attack generally exposes a weakness in rate limiting or flow. When successful this attack prevents legitimate users from accessing the service and can cause the target to crash. This attack differs from resource depletion through leaks or allocations in that the latter attacks do not rely on the volume of requests made to the target but instead focus on manipulation of the target's operations. The key factor in a flooding attack is the number of requests the adversary can make in a given period of time. The greater this number, the more likely an attack is to succeed against a given target.	Medium
130	Excessive Allocation An adversary causes the target to allocate excessive resources to servicing the attackers' request, thereby reducing the resources available for legitimate services and degrading or denying services. Usually, this attack focuses on memory allocation, but any finite resource on the target could be the attacked, including bandwidth, processing cycles, or other resources. This attack does not attempt to force this allocation through a large number of requests (that would be Resource Depletion through Flooding) but instead uses one or a small number of requests that are carefully formatted to force the target to allocate excessive resources to service this request(s). Often this attack takes advantage of a bug in the target to cause the target to allocate resources vastly beyond what would be needed for a normal request.	Medium
197	Exponential Data Expansion An adversary submits data to a target application which contains nested exponential data expansion to produce excessively large output. Many data format languages allow the definition of macro-like structures that can be used to simplify the creation of complex structures. However, this capability can be abused to create excessive demands on a processor's CPU and memory. A small number of nested expansions can result in an exponential growth in demands on memory. [Survey the target] An adversary determines the input data stream that is being processed by a data parser that supports using subsitituion on the victim's side. [Craft malicious payload] The adversary crafts a malicious message containing nested exponential expansion that completely uses up available server resources. See the "Example Instances" section for details on how to craft this malicious payload. [Send the message] Send the malicious crafted message to the target URL.	Medium
229	Serialized Data Parameter Blowup This attack exploits certain serialized data parsers (e.g., XML, YAML, etc.) which manage data in an inefficient manner. The attacker crafts an serialized data file with multiple configuration parameters in the same dataset. In a vulnerable parser, this results in a denial of service condition where CPU resources are exhausted because of the parsing algorithm. The weakness being exploited is tied to parser implementation and not language specific. [Survey the target] Using a browser or an automated tool, an attacker records all instances of web services to process requests using serialized data. [Launch a Blowup attack] The attacker crafts malicious messages that contain multiple configuration parameters in the same dataset.	High
230	Serialized Data with Nested Payloads Applications often need to transform data in and out of a data format (e.g., XML and YAML) by using a parser. It may be possible for an adversary to inject data that may have an adverse effect on the parser when it is being processed. Many data format languages allow the definition of macro-like structures that can be used to simplify the creation of complex structures. By nesting these structures, causing the data to be repeatedly substituted, an adversary can cause the parser to consume more resources while processing, causing excessive memory consumption and CPU utilization. An adversary determines the input data stream that is being processed by a data parser that supports using substitution on the victim's side. An adversary crafts input data that may have an adverse effect on the operation of the parser when the data is parsed on the victim's system.	High
231	Oversized Serialized Data Payloads An adversary injects oversized serialized data payloads into a parser during data processing to produce adverse effects upon the parser such as exhausting system resources and arbitrary code execution. An adversary determines the input data stream that is being processed by an serialized data parser on the victim's side. An adversary crafts input data that may have an adverse effect on the operation of the data parser when the data is parsed on the victim's system.	High
469	HTTP DoS An attacker performs flooding at the HTTP level to bring down only a particular web application rather than anything listening on a TCP/IP connection. This denial of service attack requires substantially fewer packets to be sent which makes DoS harder to detect. This is an equivalent of SYN flood in HTTP. The idea is to keep the HTTP session alive indefinitely and then repeat that hundreds of times. This attack targets resource depletion weaknesses in web server software. The web server will wait to attacker's responses on the initiated HTTP sessions while the connection threads are being exhausted.	Low
482	TCP Flood An adversary may execute a flooding attack using the TCP protocol with the intent to deny legitimate users access to a service. These attacks exploit the weakness within the TCP protocol where there is some state information for the connection the server needs to maintain. This often involves the use of TCP SYN messages.
486	UDP Flood An adversary may execute a flooding attack using the UDP protocol with the intent to deny legitimate users access to a service by consuming the available network bandwidth. Additionally, firewalls often open a port for each UDP connection destined for a service with an open UDP port, meaning the firewalls in essence save the connection state thus the high packet nature of a UDP flood can also overwhelm resources allocated to the firewall. UDP attacks can also target services like DNS or VoIP which utilize these protocols. Additionally, due to the session-less nature of the UDP protocol, the source of a packet is easily spoofed making it difficult to find the source of the attack.
487	ICMP Flood An adversary may execute a flooding attack using the ICMP protocol with the intent to deny legitimate users access to a service by consuming the available network bandwidth. A typical attack involves a victim server receiving ICMP packets at a high rate from a wide range of source addresses. Additionally, due to the session-less nature of the ICMP protocol, the source of a packet is easily spoofed making it difficult to find the source of the attack.
488	HTTP Flood An adversary may execute a flooding attack using the HTTP protocol with the intent to deny legitimate users access to a service by consuming resources at the application layer such as web services and their infrastructure. These attacks use legitimate session-based HTTP GET requests designed to consume large amounts of a server's resources. Since these are legitimate sessions this attack is very difficult to detect.
489	SSL Flood An adversary may execute a flooding attack using the SSL protocol with the intent to deny legitimate users access to a service by consuming all the available resources on the server side. These attacks take advantage of the asymmetric relationship between the processing power used by the client and the processing power used by the server to create a secure connection. In this manner the attacker can make a large number of HTTPS requests on a low provisioned machine to tie up a disproportionately large number of resources on the server. The clients then continue to keep renegotiating the SSL connection. When multiplied by a large number of attacking machines, this attack can result in a crash or loss of service to legitimate users.
490	Amplification An adversary may execute an amplification where the size of a response is far greater than that of the request that generates it. The goal of this attack is to use a relatively few resources to create a large amount of traffic against a target server. To execute this attack, an adversary send a request to a 3rd party service, spoofing the source address to be that of the target server. The larger response that is generated by the 3rd party service is then sent to the target server. By sending a large number of initial requests, the adversary can generate a tremendous amount of traffic directed at the target. The greater the discrepancy in size between the initial request and the final payload delivered to the target increased the effectiveness of this attack.
491	Quadratic Data Expansion An adversary exploits macro-like substitution to cause a denial of service situation due to excessive memory being allocated to fully expand the data. The result of this denial of service could cause the application to freeze or crash. This involves defining a very large entity and using it multiple times in a single entity substitution. CAPEC-197 is a similar attack pattern, but it is easier to discover and defend against. This attack pattern does not perform multi-level substitution and therefore does not obviously appear to consume extensive resources. [Survey the target] An adversary determines the input data stream that is being processed by a data parser that supports using substituion on the victim's side. [Craft malicious payload] The adversary crafts malicious message containing nested quadratic expansion that completely uses up available server resource. [Send the message] Send the malicious crafted message to the target URL.
493	SOAP Array Blowup An adversary may execute an attack on a web service that uses SOAP messages in communication. By sending a very large SOAP array declaration to the web service, the attacker forces the web service to allocate space for the array elements before they are parsed by the XML parser. The attacker message is typically small in size containing a large array declaration of say 1,000,000 elements and a couple of array elements. This attack targets exhaustion of the memory resources of the web service.
494	TCP Fragmentation An adversary may execute a TCP Fragmentation attack against a target with the intention of avoiding filtering rules of network controls, by attempting to fragment the TCP packet such that the headers flag field is pushed into the second fragment which typically is not filtered.
495	UDP Fragmentation An attacker may execute a UDP Fragmentation attack against a target server in an attempt to consume resources such as bandwidth and CPU. IP fragmentation occurs when an IP datagram is larger than the MTU of the route the datagram has to traverse. Typically the attacker will use large UDP packets over 1500 bytes of data which forces fragmentation as ethernet MTU is 1500 bytes. This attack is a variation on a typical UDP flood but it enables more network bandwidth to be consumed with fewer packets. Additionally it has the potential to consume server CPU resources and fill memory buffers associated with the processing and reassembling of fragmented packets.
496	ICMP Fragmentation An attacker may execute a ICMP Fragmentation attack against a target with the intention of consuming resources or causing a crash. The attacker crafts a large number of identical fragmented IP packets containing a portion of a fragmented ICMP message. The attacker these sends these messages to a target host which causes the host to become non-responsive. Another vector may be sending a fragmented ICMP message to a target host with incorrect sizes in the header which causes the host to hang.
528	XML Flood An adversary may execute a flooding attack using XML messages with the intent to deny legitimate users access to a web service. These attacks are accomplished by sending a large number of XML based requests and letting the service attempt to parse each one. In many cases this type of an attack will result in a XML Denial of Service (XDoS) due to an application becoming unstable, freezing, or crashing. [Survey the target] Using a browser or an automated tool, an attacker records all instance of web services to process XML requests. An adversary crafts input data that may have an adverse effect on the operation of the web service when the XML data sent to the service. [Launch a resource depletion attack] The attacker delivers a large number of XML messages to the target URLs found in the explore phase at a sufficiently rapid rate. It causes denial of service to the target application.	Medium

MITRE

Techniques

id	description
T1498.001	Network Denial of Service: Direct Network Flood
T1498.002	Network Denial of Service:Reflection Amplification
T1499	Endpoint Denial of Service
T1499.001	Endpoint Denial of Service: OS Exhaustion Flood
T1499.002	Endpoint Denial of Service: Service Exhaustion Flood
T1499.003	Endpoint Denial of Service:Application Exhaustion Flood
© 2022 The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation.

Mitigations

id	description
M1037	When flood volumes exceed the capacity of the network connection being targeted, it is typically necessary to intercept the incoming traffic upstream to filter out the attack traffic from the legitimate traffic. Such defenses can be provided by the hosting Internet Service Provider (ISP) or by a 3rd party such as a Content Delivery Network (CDN) or providers specializing in DoS mitigations. Depending on flood volume, on-premises filtering may be possible by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. As immediate response may require rapid engagement of 3rd parties, analyze the risk associated to critical resources being affected by Network DoS attacks and create a disaster recovery plan/business continuity plan to respond to incidents.
M1037	When flood volumes exceed the capacity of the network connection being targeted, it is typically necessary to intercept the incoming traffic upstream to filter out the attack traffic from the legitimate traffic. Such defenses can be provided by the hosting Internet Service Provider (ISP) or by a 3rd party such as a Content Delivery Network (CDN) or providers specializing in DoS mitigations. Depending on flood volume, on-premises filtering may be possible by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. As immediate response may require rapid engagement of 3rd parties, analyze the risk associated to critical resources being affected by Network DoS attacks and create a disaster recovery plan/business continuity plan to respond to incidents.
M1037	Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. To defend against SYN floods, enable SYN Cookies.
M1037	Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. To defend against SYN floods, enable SYN Cookies.
M1037	Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport.
M1037	Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport.
© 2022 The MITRE Corporation. Esta obra se reproduce y distribuye con el permiso de The MITRE Corporation.

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