CVE-2025-32393 Vulnerability Analysis & Exploit Details

CVE-2025-32393
Vulnerability Scoring

6.5
/10
High Risk

If left unpatched, CVE-2025-32393 could lead to major system disruptions or data loss.

Attack Complexity Details

  • Attack Complexity: Low
    Exploits can be performed without significant complexity or special conditions.
  • Attack Vector: Network
    Vulnerability is exploitable over a network without physical access.
  • Privileges Required: Low
    Some privileges are necessary to exploit the vulnerability.
  • Scope: Unchanged
    Exploit remains within the originally vulnerable component.
  • User Interaction: None
    No user interaction is necessary for exploitation.

CVE-2025-32393 Details

Status: Analyzed

Last updated: 🕞 17 Feb 2026, 15:40 UTC
Originally published on: 🕚 05 Feb 2026, 23:15 UTC

Time between publication and last update: 11 days

CVSS Release: version 3

CVSS3 Source

nvd@nist.gov

CVSS3 Type

Primary

CVSS3 Vector

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

CVE-2025-32393 Vulnerability Summary

CVE-2025-32393: AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Prior to autogpt-platform-beta-v0.6.32, there is a DoS vulnerability in ReadRSSFeedBlock. In RSSBlock, feedparser.parser is called to obtain the XML file according to the URL input by the user, parse the XML, and finally obtain the parsed result. However, during the parsing process, there is no limit on the parsing time and the resources that can be allocated for parsing. When a malicious user lets RSSBlock parse a carefully constructed, deep XML, it will cause memory resources to be exhausted, eventually causing DoS. This issue has been patched in autogpt-platform-beta-v0.6.32.

Assessing the Risk of CVE-2025-32393

Access Complexity Graph

The exploitability of CVE-2025-32393 depends on two key factors: attack complexity (the level of effort required to execute an exploit) and privileges required (the access level an attacker needs).

Exploitability Analysis for CVE-2025-32393

CVE-2025-32393 presents an accessible attack vector with minimal effort required. Restricting access controls and implementing security updates are critical to reducing exploitation risks.

Understanding AC and PR

A lower complexity and fewer privilege requirements make exploitation easier. Security teams should evaluate these aspects to determine the urgency of mitigation strategies, such as patch management and access control policies.

Attack Complexity (AC) measures the difficulty in executing an exploit. A high AC means that specific conditions must be met, making an attack more challenging, while a low AC means the vulnerability can be exploited with minimal effort.

Privileges Required (PR) determine the level of system access necessary for an attack. Vulnerabilities requiring no privileges are more accessible to attackers, whereas high privilege requirements limit exploitation to authorized users with elevated access.

CVSS Score Breakdown Chart

Above is the CVSS Sub-score Breakdown for CVE-2025-32393, illustrating how Base, Impact, and Exploitability factors combine to form the overall severity rating. A higher sub-score typically indicates a more severe or easier-to-exploit vulnerability.

CIA Impact Analysis

Below is the Impact Analysis for CVE-2025-32393, showing how Confidentiality, Integrity, and Availability might be affected if the vulnerability is exploited. Higher values usually signal greater potential damage.

  • Confidentiality: None
    CVE-2025-32393 has no significant impact on data confidentiality.
  • Integrity: None
    CVE-2025-32393 poses no threat to data integrity.
  • Availability: High
    CVE-2025-32393 can disrupt system operations, potentially causing complete denial of service (DoS).

CVE-2025-32393 References

External References

CWE Common Weakness Enumeration

CWE-770

CAPEC Common Attack Pattern Enumeration and Classification

  • Flooding CAPEC-125 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.
  • Excessive Allocation CAPEC-130 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.
  • XML Ping of the Death CAPEC-147 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.
  • Exponential Data Expansion CAPEC-197 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.
  • Serialized Data Parameter Blowup CAPEC-229 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.
  • Serialized Data with Nested Payloads CAPEC-230 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.
  • Oversized Serialized Data Payloads CAPEC-231 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.
  • HTTP DoS CAPEC-469 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.
  • TCP Flood CAPEC-482 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.
  • UDP Flood CAPEC-486 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.
  • ICMP Flood CAPEC-487 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.
  • HTTP Flood CAPEC-488 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.
  • SSL Flood CAPEC-489 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.
  • Amplification CAPEC-490 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.
  • Quadratic Data Expansion CAPEC-491 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.
  • SOAP Array Blowup CAPEC-493 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.
  • TCP Fragmentation CAPEC-494 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.
  • UDP Fragmentation CAPEC-495 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.
  • ICMP Fragmentation CAPEC-496 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.
  • XML Flood CAPEC-528 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.

Vulnerable Configurations

  • cpe:2.3:a:agpt:autogpt_platform:0.1.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.1.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.1.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.1.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.2.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.2.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.2.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.2.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.2.2:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.2.2:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.3.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.3.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.3.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.3.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.3.2:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.3.2:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.3.3:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.3.3:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.3.4:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.3.4:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.2:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.2:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.3:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.3:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.4:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.4:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.5:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.5:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.6:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.6:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.7:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.7:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.8:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.8:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.9:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.9:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.10:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.10:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.4.11:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.4.11:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.5.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.5.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.5.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.5.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.0:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.0:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.1:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.1:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.2:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.2:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.3:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.3:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.4:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.4:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.5:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.5:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.6:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.6:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.7:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.7:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.8:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.8:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.9:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.9:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.10:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.10:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.11:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.11:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.12:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.12:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.13:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.13:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.14:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.14:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.15:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.15:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.16:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.16:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.17:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.17:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.18:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.18:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.19:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.19:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.20:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.20:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.21:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.21:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.22:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.22:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.23:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.23:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.24:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.24:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.25:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.25:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.26:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.26:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.27:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.27:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.28:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.28:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.29:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.29:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.30:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.30:beta:*:*:*:*:*:*
  • cpe:2.3:a:agpt:autogpt_platform:0.6.31:beta:*:*:*:*:*:*
    cpe:2.3:a:agpt:autogpt_platform:0.6.31:beta:*:*:*:*:*:*

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