Threat: Data Poisoning
An attacker injects malicious or manipulated vector data into the Faiss index during index creation or updates. This is achieved by compromising data sources or data ingestion pipelines before the data reaches Faiss for indexing. The attacker aims to manipulate Faiss search results by adding vectors that are designed to be retrieved for unrelated queries or to bias search outcomes, directly impacting the accuracy and reliability of Faiss-powered search.
Threat: Index Corruption/Tampering
An attacker gains unauthorized access to the stored Faiss index file or memory representation and directly modifies or corrupts the index data. This could be through compromised system access or exploiting vulnerabilities in storage mechanisms external to Faiss, but directly impacting the integrity of the Faiss index. The attacker aims to disrupt Faiss search service availability or manipulate search results by altering the index structure or vector data, directly attacking the core data structure Faiss relies on.
An attacker crafts specific search queries or exploits certain Faiss index types or search parameters that are known to be computationally expensive. By sending a high volume of these crafted queries, the attacker can overload the system's resources (CPU, memory, I/O) due to Faiss's internal algorithms, leading to performance degradation or denial of service. This directly exploits the computational characteristics of Faiss algorithms.
Faiss, being a complex C++ library, may contain security vulnerabilities such as buffer overflows, memory corruption bugs, or other coding errors. Attackers could exploit these vulnerabilities by crafting specific inputs or triggering vulnerable code paths through Faiss API calls or data manipulation directed at Faiss. Successful exploitation can lead to remote code execution, denial of service, or information disclosure within the Faiss process or the application using it. This is a direct threat stemming from the security of the Faiss codebase itself.