CVE-2024-57823 is a critical integer underflow vulnerability classified under CWE-191, found in the Raptor RDF Syntax Library (librdf) up to version 2.0.16. The vulnerability occurs in the raptor_uri_normalize_path() function, which is responsible for normalizing URIs when parsing Turtle syntax RDF data. During this normalization process, an integer underflow can happen if certain crafted URIs are processed, causing the internal length or index calculations to wrap around unexpectedly. This underflow can lead to buffer overflows or memory corruption, potentially allowing an attacker to execute arbitrary code, crash the application, or manipulate data integrity. The vulnerability has a CVSS v3.1 base score of 9.3, reflecting its critical nature with high impact on confidentiality, integrity, and availability. The attack vector is local (AV:L), but it requires no privileges (PR:N) or user interaction (UI:N), and the scope is changed (S:C), meaning the vulnerability can affect resources beyond the vulnerable component. Although no exploits are currently known in the wild, the vulnerability poses a significant risk to applications and services that utilize librdf for RDF data processing, especially those parsing untrusted input. The lack of available patches at the time of publication necessitates cautious handling of affected systems and monitoring for updates.

For European organizations, the impact of CVE-2024-57823 can be substantial, particularly for entities involved in semantic web technologies, linked data, and RDF data processing. Compromise of systems using the vulnerable Raptor library could lead to unauthorized data disclosure, data manipulation, or service disruption. This is especially critical for research institutions, government agencies, and enterprises relying on RDF for data integration, metadata management, or knowledge graphs. The vulnerability's ability to affect confidentiality, integrity, and availability means sensitive data could be exposed or corrupted, and critical services could be interrupted. Given the local attack vector, exploitation might require some level of access, but the absence of required privileges or user interaction lowers the barrier for attackers who can supply malicious RDF data. The potential for remote exploitation depends on how the library is integrated into network-facing applications. Overall, the threat could undermine trust in data-driven applications and disrupt operations in sectors such as finance, healthcare, and public administration across Europe.

Immediate mitigation should focus on restricting access to systems processing untrusted RDF data with the vulnerable librdf versions. Organizations should implement strict input validation and sanitization for RDF inputs to prevent malicious URI normalization attempts. Monitoring and logging of RDF parsing errors or crashes can help detect exploitation attempts. Until an official patch is released, consider isolating or sandboxing applications using the Raptor library to limit potential damage from exploitation. Engage with software vendors or open-source maintainers to obtain or contribute to patches addressing the integer underflow. Additionally, conduct thorough code reviews and security testing of custom applications integrating librdf to identify and remediate unsafe URI handling. Network-level protections, such as firewalls and intrusion detection systems, should be tuned to detect anomalous traffic patterns related to RDF data ingestion. Finally, maintain an incident response plan tailored to address potential exploitation scenarios involving semantic web technologies.