Focus on llamaindex vulnerabilities and metrics.
Last updated: 01 Aug 2025, 22:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with llamaindex. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total llamaindex CVEs: 15
Earliest CVE date: 22 Jan 2024, 01:15 UTC
Latest CVE date: 10 Jul 2025, 13:15 UTC
Latest CVE reference: CVE-2025-6211
30-day Count (Rolling): 5
365-day Count (Rolling): 13
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): 400.0%
Year Variation (Calendar): 550.0%
Month Growth Rate (30-day Rolling): 400.0%
Year Growth Rate (365-day Rolling): 550.0%
Average CVSS: 0.0
Max CVSS: 0
Critical CVEs (≥9): 0
| Range | Count |
|---|---|
| 0.0-3.9 | 15 |
| 4.0-6.9 | 0 |
| 7.0-8.9 | 0 |
| 9.0-10.0 | 0 |
These are the five CVEs with the highest CVSS scores for llamaindex, sorted by severity first and recency.
A vulnerability in the DocugamiReader class of the run-llama/llama_index repository, up to version 0.12.28, involves the use of MD5 hashing to generate IDs for document chunks. This approach leads to hash collisions when structurally distinct chunks contain identical text, resulting in one chunk overwriting another. This can cause loss of semantically or legally important document content, breakage of parent-child chunk hierarchies, and inaccurate or hallucinated responses in AI outputs. The issue is resolved in version 0.3.1.
A vulnerability in the ObsidianReader class of the run-llama/llama_index repository, specifically in version 0.12.27, allows for hardlink-based path traversal. This flaw permits attackers to bypass path restrictions and access sensitive system files, such as /etc/passwd, by exploiting hardlinks. The vulnerability arises from inadequate handling of hardlinks in the load_data() method, where the security checks fail to differentiate between real files and hardlinks. This issue is resolved in version 0.5.2.
An XML Entity Expansion vulnerability, also known as a 'billion laughs' attack, exists in the sitemap parser of the run-llama/llama_index repository, specifically affecting version v0.12.21. This vulnerability allows an attacker to supply a malicious Sitemap XML, leading to a Denial of Service (DoS) by exhausting system memory and potentially causing a system crash. The issue is resolved in version v0.12.29.
A vulnerability in the `ObsidianReader` class of the run-llama/llama_index repository, versions 0.12.23 to 0.12.28, allows for arbitrary file read through symbolic links. The `ObsidianReader` fails to resolve symlinks to their real paths and does not validate whether the resolved paths lie within the intended directory. This flaw enables attackers to place symlinks pointing to files outside the vault directory, which are then processed as valid Markdown files, potentially exposing sensitive information.
A vulnerability in the ArxivReader class of the run-llama/llama_index repository, versions up to v0.12.22.post1, allows for MD5 hash collisions when generating filenames for downloaded papers. This can lead to data loss as papers with identical titles but different contents may overwrite each other, preventing some papers from being processed for AI model training. The issue is resolved in version 0.12.28.
Multiple vector store integrations in run-llama/llama_index version v0.12.21 have SQL injection vulnerabilities. These vulnerabilities allow an attacker to read and write data using SQL, potentially leading to unauthorized access to data of other users depending on the usage of the llama-index library in a web application.
An SQL injection vulnerability exists in the delete function of DuckDBVectorStore in run-llama/llama_index version v0.12.19. This vulnerability allows an attacker to manipulate the ref_doc_id parameter, enabling them to read and write arbitrary files on the server, potentially leading to remote code execution (RCE).
A Denial of Service (DoS) vulnerability has been identified in the KnowledgeBaseWebReader class of the run-llama/llama_index project, affecting version ~ latest(v0.12.15). The vulnerability arises due to inappropriate secure coding measures, specifically the lack of proper implementation of the max_depth parameter in the get_article_urls function. This allows an attacker to exhaust Python's recursion limit through repeated function calls, leading to resource consumption and ultimately crashing the Python process.
A vulnerability in the `default_jsonalyzer` function of the `JSONalyzeQueryEngine` in the run-llama/llama_index repository allows for SQL injection via prompt injection. This can lead to arbitrary file creation and Denial-of-Service (DoS) attacks. The vulnerability affects the latest version and is fixed in version 0.5.1.
A vulnerability in the `KnowledgeBaseWebReader` class of the run-llama/llama_index repository, version latest, allows an attacker to cause a Denial of Service (DoS) by controlling a URL variable to contain the root URL. This leads to infinite recursive calls to the `get_article_urls` method, exhausting system resources and potentially crashing the application.
A vulnerability in the FinanceChatLlamaPack of the run-llama/llama_index repository, versions up to v0.12.3, allows for SQL injection in the `run_sql_query` function of the `database_agent`. This vulnerability can be exploited by an attacker to inject arbitrary SQL queries, leading to remote code execution (RCE) through the use of PostgreSQL's large object functionality. The issue is fixed in version 0.3.0.
A vulnerability in the LangChainLLM class of the run-llama/llama_index repository, version v0.12.5, allows for a Denial of Service (DoS) attack. The stream_complete method executes the llm using a thread and retrieves the result via the get_response_gen method of the StreamingGeneratorCallbackHandler class. If the thread terminates abnormally before the _llm.predict is executed, there is no exception handling for this case, leading to an infinite loop in the get_response_gen function. This can be triggered by providing an input of an incorrect type, causing the thread to terminate and the process to continue running indefinitely.
A SQL injection vulnerability exists in the `duckdb_retriever` component of the run-llama/llama_index repository, specifically in the latest version. The vulnerability arises from the construction of SQL queries without using prepared statements, allowing an attacker to inject arbitrary SQL code. This can lead to remote code execution (RCE) by installing the shellfs extension and executing malicious commands.
A command injection vulnerability exists in the run-llama/llama_index repository, specifically within the safe_eval function. Attackers can bypass the intended security mechanism, which checks for the presence of underscores in code generated by LLM, to execute arbitrary code. This is achieved by crafting input that does not contain an underscore but still results in the execution of OS commands. The vulnerability allows for remote code execution (RCE) on the server hosting the application.
LlamaIndex (aka llama_index) through 0.9.34 allows SQL injection via the Text-to-SQL feature in NLSQLTableQueryEngine, SQLTableRetrieverQueryEngine, NLSQLRetriever, RetrieverQueryEngine, and PGVectorSQLQueryEngine. For example, an attacker might be able to delete this year's student records via "Drop the Students table" within English language input.