Objective: To compromise a FAISS-using application by exploiting weaknesses or vulnerabilities within the FAISS component, specifically aiming to cause denial of service (DoS) or manipulate search results.
Compromise FAISS-Using Application
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Denial of Service (FAISS-Specific) Manipulate Search Results
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Index Corruption/Poisoning Resource Exhaustion Index Poisoning/ Corruption
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Index File ->HIGH RISK-> Index File ->HIGH RISK-> CPU-Intensive Index File
Corruption Poisoning Queries Corruption
[CRITICAL] (M/H/M-H/A/M-H) [CRITICAL] (M-H/M-H/L-M/N-I/M) [CRITICAL]
(L/H/L/N/E) (L/H/L/N/E)
Attack Tree Path: Denial of Service (FAISS-Specific) - Index Corruption/Poisoning - Index File Corruption [CRITICAL]
Description: The attacker gains unauthorized access to the file system where the FAISS index is stored and directly modifies the index file, corrupting its structure and rendering it unusable by FAISS. Likelihood: Low (Requires file system access, which should be heavily restricted) Impact: High (Renders the entire FAISS index unusable, causing a complete denial of service for the similarity search functionality) Effort: Low (Once file system access is obtained, corrupting the file is trivial) Skill Level: Novice (Basic file manipulation skills are sufficient) Detection Difficulty: Easy (FAISS will fail to load the corrupted index, generating clear error messages) Mitigation: * Implement strong file system security and access controls (Principle of Least Privilege). * Use a dedicated, restricted user account for the application accessing FAISS. * Consider file integrity monitoring (FIM) to detect unauthorized modifications. * Store index files on a separate, secure volume if possible. * Regularly back up the index file.
Attack Tree Path: Denial of Service (FAISS-Specific) - Index Corruption/Poisoning - -> HIGH RISK -> Index File Poisoning
Description: The attacker exploits a vulnerability in the application that allows them to inject malicious vectors into the FAISS index during the index building or updating process. These malicious vectors are designed to corrupt the index structure or make it extremely inefficient, leading to denial of service. Likelihood: Medium (Depends on whether the application allows untrusted input to influence the index building process) Impact: High (Can make the index unusable or so slow that it effectively becomes unavailable) Effort: Medium to High (Requires crafting malicious vectors and finding a way to inject them into the index) Skill Level: Advanced (Requires understanding of FAISS index structures and how to craft vectors that cause corruption or inefficiency) Detection Difficulty: Medium to Hard (Requires analyzing the index structure and performance to detect anomalies; may require comparing against a known good index) Mitigation: * Strictly validate and sanitize all input vectors before adding them to the index. Check data type, dimensionality, range, and any other relevant properties. * Implement input filtering to reject vectors that are known to be problematic or that deviate significantly from the expected distribution. * Consider using a separate, trusted process for index building and updating, isolated from the main application. * Regularly audit the index building/updating process for vulnerabilities.
Attack Tree Path: Denial of Service (FAISS-Specific) - Resource Exhaustion - -> HIGH RISK -> CPU-Intensive Queries
Description: The attacker sends a large number of queries, or specially crafted queries, that force FAISS to perform an excessive amount of computation, exhausting CPU resources and causing the FAISS component to become slow or unresponsive. This is particularly effective against brute-force search methods or complex distance calculations. Likelihood: Medium to High (Depends on the type of FAISS index used and whether query limits are in place) Impact: Medium to High (Can significantly degrade performance or cause the FAISS component to crash, leading to denial of service) Effort: Low to Medium (Can often be achieved with relatively simple queries, especially if the index uses brute-force search) Skill Level: Novice to Intermediate (Basic understanding of FAISS querying is sufficient; crafting highly optimized attack queries requires more skill) Detection Difficulty: Medium (High CPU usage might be detected, but distinguishing from legitimate use can be challenging, especially if the attacker uses a distributed attack) Mitigation: * Implement query rate limiting and throttling to prevent attackers from flooding the system with requests. * Set resource limits on FAISS queries (CPU time, memory usage). * Use more efficient index types (e.g., HNSW) instead of brute-force search whenever possible. * Monitor CPU usage and query latency to detect anomalies. * Consider using a Web Application Firewall (WAF) to filter out malicious requests.
Attack Tree Path: Manipulate Search Results - Index Corruption/Poisoning - Index File Corruption [CRITICAL]
Description: The attacker gains unauthorized access to the file system and subtly modifies the FAISS index file. Unlike the DoS scenario, the goal here is not to make the index unusable, but to alter the distances between vectors or the graph structure (in HNSW) in a way that leads to incorrect search results. Likelihood: Low (Requires file system access and precise manipulation) Impact: Medium to High (Can cause the application to return incorrect or misleading results, potentially leading to incorrect decisions or actions) Effort: High (Requires a deep understanding of the FAISS index format and the ability to make precise, targeted modifications without causing obvious errors) Skill Level: Expert (Requires advanced knowledge of FAISS internals and binary file manipulation) Detection Difficulty: Very Hard (Requires sophisticated analysis of the index and search results, potentially comparing against a known good index or using statistical methods to detect anomalies) Mitigation: * Implement strong file system security and access controls (Principle of Least Privilege). * Use a dedicated, restricted user account for the application accessing FAISS. * Implement file integrity monitoring (FIM) with very sensitive alerting. * Store index files on a separate, secure volume if possible. * Regularly back up the index file and compare against backups to detect subtle changes. * Consider using cryptographic signatures to verify the integrity of the index file.