FQE-1715 - Microsoft Access handler error

[setohe0909]

Description

• Expanded unit tests for the Access handler integration.
• Conditional dependency: sqlalchemy-access is only installed on Windows (sys_platform == 'win32') since Access databases are Windows-specific
• Cross-platform testing: Mocks allow the tests to run on non-Windows systems

Fixes FQE-1715

Unit tests

Type of change

• 🐛 Bug fix (non-breaking change which fixes an issue)

Verification Process

To ensure the changes are working as expected:

• Test Location: Specify the URL or path for testing.
• Verification Steps: Outline the steps or queries needed to validate the change. Include any data, configurations, or actions required to reproduce or see the new functionality.

Checklist:

• My code follows the style guidelines(PEP 8) of MindsDB.
• I have appropriately commented on my code, especially in complex areas.
• Necessary documentation updates are either made or tracked in issues.
• Relevant unit and integration tests are updated or added.

[entelligence-ai-pr-reviews]

🔒 Entelligence AI Vulnerability Scanner

✅ No security vulnerabilities found!

Your code passed our comprehensive security analysis.

📊 Files Analyzed: 3 files

---

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (1)

Skipped posting 1 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/access_handler/access_handler.py (1)

160-181: get_tables and get_columns use list comprehensions to build DataFrames from potentially large schema queries, which can cause high memory and CPU usage for databases with many tables/columns.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 2/5
• Urgency Impact: 2/5
• Total Score: 6/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/access_handler/access_handler.py, lines 160-181, replace the list comprehensions in `get_tables` and `get_columns` with explicit for-loops to build the lists before creating the DataFrames. This reduces peak memory usage and improves performance for large schema queries.

---

[entelligence-ai-pr-reviews]

File: mindsdb/integrations/handlers/access_handler/access_handler.py (Lines 104-136)

security: native_query executes raw SQL queries from untrusted input without sanitization or parameterization, enabling SQL injection attacks that can compromise or destroy database contents.

🤖 AI Agent Prompt for Cursor/Windsurf

📋 Copy this prompt to your AI coding assistant (Cursor, Windsurf, etc.) to get help fixing this issue

In mindsdb/integrations/handlers/access_handler/access_handler.py, lines 104-136, the `native_query` method executes raw SQL queries directly from user input, making it vulnerable to SQL injection. Refactor this method to use parameterized queries by accepting a `params` argument (tuple) and passing it to `cursor.execute(query, params)`. Update all usages accordingly to prevent SQL injection.

📝 Committable Code Suggestion

‼️ Ensure you review the code suggestion before committing it to the branch. Make sure it replaces the highlighted code, contains no missing lines, and has no issues with indentation.

    def native_query(self, query: str, params: tuple = ()) -> StatusResponse:
        """
        Receive raw query and act upon it somehow.
        Args:
            query (str): query in native format
            params (tuple): parameters for the query
        Returns:
            HandlerResponse
        """

        need_to_close = self.is_connected is False
        connection = self.connect()
        with connection.cursor() as cursor:
            try:
                cursor.execute(query, params)
                result = cursor.fetchall()
                if result:
                    response = Response(
                        RESPONSE_TYPE.TABLE,
                        data_frame=pd.DataFrame.from_records(result, columns=[x[0] for x in cursor.description]),
                    )
                else:
                    response = Response(RESPONSE_TYPE.OK)
                    connection.commit()
            except Exception as e:
                logger.error(f"Error running query: {query} on {self.connection_data['db_file']}!")
                response = Response(RESPONSE_TYPE.ERROR, error_message=str(e))

        if need_to_close is True:
            self.disconnect()

        return response

---

Note: This comment was posted as a general PR comment because the specific line could not be resolved in the diff.

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (20)

Skipped posting 20 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/__main__.py (2)

180-189: Calling db.session.commit() inside a loop in set_error_model_status_by_pids and set_error_model_status_for_unfinished causes N database commits, leading to major performance degradation for large numbers of models.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Optimize database commit performance in mindsdb/__main__.py lines 180-189: The function `set_error_model_status_by_pids` currently calls `db.session.commit()` inside a loop, causing N commits for N models. Refactor so that all updates are made in the loop, and a single `db.session.commit()` is called after the loop if any records were updated.

---

133-148: The try-except block inside a loop in close_api_gracefully (lines 133-148) incurs significant performance overhead and can mask errors; process child termination should be batched and exceptions handled outside the loop.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 4/5
• Urgency Impact: 2/5
• Total Score: 8/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/__main__.py lines 133-148, refactor `close_api_gracefully` to avoid using a `try`-`except` block inside the loop over child processes. Instead, handle exceptions outside the loop and only where necessary, to reduce performance overhead and avoid masking errors.

---

mindsdb/api/executor/datahub/datanodes/system_tables.py (6)

528-535: pd.concat([df, ...]) is used repeatedly in a loop to build large DataFrames, causing O(n^2) time/memory usage for many databases/tables.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 528-535, the code uses `df = pd.concat([df, table_df])` inside a loop, which leads to quadratic time/memory usage for many databases/tables. Refactor this to collect all DataFrames in a list and concatenate once at the end for O(n) performance. Replace the repeated concat with a list accumulation and a single pd.concat call.

---

565-570: Repeated pd.concat([df, ...]) in a loop for columns/statistics/constraints causes O(n^2) time/memory usage as data scales.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 565-570, the code uses `df = pd.concat([df, columns_df])` inside a loop, which is inefficient for large numbers of databases/tables. Refactor to accumulate DataFrames in a list and concatenate once at the end for better performance.

---

602-607: Multiple pd.concat([df, ...]) in loops for statistics/constraints/usage tables leads to quadratic performance as data grows.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 602-607, the code repeatedly concatenates DataFrames in a loop, causing O(n^2) performance. Refactor to collect all DataFrames in a list and concatenate once at the end.

---

647-684: In MetaTableConstraintsTable.get_data, repeated pd.concat([df, ...]) in a loop for primary/foreign keys causes O(n^2) time/memory usage.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 647-684, the code uses repeated `pd.concat([df, ...])` in a loop for primary/foreign key DataFrames, causing quadratic performance. Refactor to accumulate all DataFrames in a list and concatenate once at the end.

---

715-757: In MetaColumnUsageTable.get_data, repeated pd.concat([df, ...]) in a loop for primary/foreign keys causes O(n^2) time/memory usage.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 715-757, the code uses repeated `pd.concat([df, ...])` in a loop for primary/foreign key DataFrames, causing quadratic performance. Refactor to accumulate all DataFrames in a list and concatenate once at the end.

---

777-779: MetaHandlerInfoTable.get_data returns handler info as a string from DataCatalogRetriever.retrieve_handler_info() without sanitization, risking sensitive backend error or configuration data exposure to unprivileged users.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 4/5
• Urgency Impact: 4/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/api/executor/datahub/datanodes/system_tables.py, lines 777-779, the code exposes potentially sensitive handler info (including backend errors, credentials, or stack traces) to users via the HANDLER_INFO column. Update this code to only include non-sensitive, non-error fields (exclude keys like 'error', 'traceback', 'credentials', 'password', 'secret', 'token') from the handler_info dict before converting to string and returning. If handler_info is not a dict or is empty, return None for HANDLER_INFO.

---

mindsdb/integrations/handlers/mysql_handler/mysql_handler.py (1)

87-102: _make_table_response builds a list of pandas Series for each column by iterating over all rows per column, resulting in O(n*m) operations and high memory usage for large result sets.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Optimize the `_make_table_response` function in mindsdb/integrations/handlers/mysql_handler/mysql_handler.py (lines 87-102). The current code builds a list of pandas Series by iterating over all rows for each column, which is O(n*m) and inefficient for large result sets. Refactor to construct the DataFrame directly from the result list, then cast columns to the appropriate dtype in a single pass. Preserve all original logic and ensure the output DataFrame is functionally equivalent.

---

mindsdb/integrations/utilities/rag/settings.py (1)

771-771: id_key in RAGPipelineModel is typed as int but defaults to a string (DEFAULT_ID_KEY), which will cause runtime type errors or incorrect behavior when used as an integer.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/utilities/rag/settings.py, line 771, the field `id_key` in `RAGPipelineModel` is typed as `int` but is assigned a string default (`DEFAULT_ID_KEY`). Change its type annotation from `int` to `str` to match the default and prevent runtime errors.

---

mindsdb/interfaces/agents/agents_controller.py (2)

0326-0353: skills can be None, causing a TypeError when iterated in for skill in skills: if no skills or include_tables/knowledge_bases are provided.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/interfaces/agents/agents_controller.py, lines 326-353, the code iterates over `skills` without checking if it is None. This can cause a TypeError if no skills or include_tables/include_knowledge_bases are provided. Add a check to set `skills = []` if it is falsy before the for loop. Update the code so that it is safe to iterate over `skills`.

---

197-358: add_agent and update_agent methods are excessively large and complex (over 50 statements, 20+ branches), making them hard to maintain and error-prone as the codebase grows.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 3/5
• Urgency Impact: 2/5
• Total Score: 7/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the `add_agent` method in mindsdb/interfaces/agents/agents_controller.py (lines 197-358) to reduce complexity and improve maintainability. Break it into smaller helper methods for: parameter validation, model/provider resolution, skill processing, and agent creation. Ensure each helper has a single responsibility and the main method orchestrates the flow. Preserve all existing logic and functionality.

---

mindsdb/interfaces/data_catalog/data_catalog_retriever.py (2)

177-184: _construct_metadata_string_for_columns iterates over all columns and for each, filters column_stats_df by column name, resulting in O(n*m) time for n columns and m stats; this is inefficient for large tables.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 9/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Optimize performance in mindsdb/interfaces/data_catalog/data_catalog_retriever.py lines 177-184: The current code filters column_stats_df for each column, resulting in O(n*m) time. Refactor to build a mapping from column name to stats row before the loop, so each lookup is O(1). Replace the per-iteration filter with a dictionary lookup.

---

163-163: _construct_metadata_string_for_primary_keys uses inplace=True in sort_values, which can cause pandas SettingWithCopy warnings and unnecessary memory usage for large DataFrames.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 9/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/interfaces/data_catalog/data_catalog_retriever.py at line 163, avoid using inplace=True in pandas sort_values due to potential SettingWithCopy warnings and memory inefficiency. Instead, assign the sorted DataFrame back to the variable.

---

mindsdb/interfaces/database/integrations.py (1)

287-294: The get_all method loads all integration records and processes each in a loop, which can cause significant memory and CPU usage if there are thousands of integrations.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 4/5
• Urgency Impact: 2/5
• Total Score: 9/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Optimize the `get_all` method in mindsdb/interfaces/database/integrations.py (lines 287-294). The current implementation loads all Integration ORM objects and processes them in a loop, which can cause high memory and CPU usage for large numbers of integrations. Refactor to fetch only the necessary columns using a query for tuples or namedtuples, and process them efficiently to reduce memory footprint and improve scalability.

---

mindsdb/interfaces/knowledge_base/controller.py (1)

259-433: select method (lines 259-433) is excessively large and complex, with deeply nested logic and many branches, making it hard to maintain and optimize for performance as the codebase grows.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 3/5
• Urgency Impact: 2/5
• Total Score: 7/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the `select` method in mindsdb/interfaces/knowledge_base/controller.py (lines 259-433). The function is excessively large and complex, with deeply nested logic and many branches, making it difficult to maintain and optimize. Break it into smaller, well-named helper methods for each major logical block (e.g., query parsing, hybrid search, reranking, result post-processing). Ensure the refactor preserves all existing functionality and performance characteristics.

---

mindsdb/interfaces/skills/sql_agent.py (1)

464-485: get_table_info retrieves metadata for each table individually in a loop without caching, causing repeated expensive I/O/database calls for the same tables across invocations, which can significantly degrade performance for large table sets.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 4/5
• Urgency Impact: 3/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/interfaces/skills/sql_agent.py, lines 464-485, the `get_table_info` method retrieves metadata for each table individually in a loop without caching, causing repeated expensive I/O/database calls for the same tables across invocations. Refactor this block to cache the metadata retrieval results per (database, table_names) combination using the available self._cache (if present), so repeated calls for the same tables are fast and avoid redundant I/O.

---

mindsdb/interfaces/storage/db.py (2)

43-66: session and engine are global variables set in init() but not protected against concurrent access, risking race conditions and resource contention under high load.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 5/5
• Urgency Impact: 3/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/interfaces/storage/db.py, lines 43-66, the global variables `session` and `engine` are set in the `init()` function without any concurrency protection. This can cause race conditions and resource contention if `init()` is called from multiple threads, especially under high load. Refactor this section to use a threading.Lock to protect the initialization of `session` and `engine`, ensuring thread safety. Add the lock at the module level and wrap the initialization logic inside `init()` with it.

---

70-93: serializable_insert uses a busy-wait loop with no delay, causing high CPU usage under contention and risking resource exhaustion.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 4/5
• Urgency Impact: 3/5
• Total Score: 11/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/interfaces/storage/db.py, lines 70-93, the `serializable_insert` function uses a busy-wait loop with no delay when retrying after an OperationalError. This can cause high CPU usage and resource exhaustion under contention. Refactor the loop to implement exponential backoff (e.g., start with 10ms and double up to 1s) between retries to reduce CPU load and improve system stability.

---

mindsdb/utilities/config.py (1)

222-375: prepare_env_config is excessively complex (32 branches, 81 statements), making it hard to maintain and error-prone as config logic grows.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 3/5
• Urgency Impact: 2/5
• Total Score: 7/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the `prepare_env_config` method in mindsdb/utilities/config.py (lines 222-375). The function is too complex (32 branches, 81 statements), making it hard to maintain and error-prone. Extract each major environment variable group (paths, storage, ml queue, auth, logging, reranker, gui, etc.) into separate helper methods, and call them from `prepare_env_config`. This will reduce cyclomatic complexity and improve maintainability. Do not change the logic, only the structure.

---

🔍 Comments beyond diff scope (3)

mindsdb/integrations/handlers/mysql_handler/mysql_handler.py (2)

298-316: get_columns constructs a SQL query using direct string interpolation of table_name, allowing SQL injection if table_name is attacker-controlled.
Category: security

---

393-483: meta_get_column_statistics builds a SQL query with table_names interpolated directly, allowing SQL injection if table_names is attacker-controlled.
Category: security

---

mindsdb/interfaces/skills/sql_agent.py (1)

526-527,596-597: get_kb_sample_rows and _get_sample_rows construct SQL queries by directly interpolating kb_name or table into the query string, allowing SQL injection if these values are attacker-controlled.
Category: security

---

[entelligence-ai-pr-reviews]

File: mindsdb/interfaces/data_catalog/data_catalog_retriever.py (Lines 325-327)

correctness: retrieve_tables, retrieve_columns, retrieve_column_statistics, retrieve_primary_keys, and retrieve_foreign_keys treat RESPONSE_TYPE.OK as an error, causing valid data to be ignored and always returning empty DataFrames.

🤖 AI Agent Prompt for Cursor/Windsurf

📋 Copy this prompt to your AI coding assistant (Cursor, Windsurf, etc.) to get help fixing this issue

In mindsdb/interfaces/data_catalog/data_catalog_retriever.py, lines 325-327, 344-346, 362-364, 379-381, and 396-398, the methods `retrieve_tables`, `retrieve_columns`, `retrieve_column_statistics`, `retrieve_primary_keys`, and `retrieve_foreign_keys` incorrectly treat `RESPONSE_TYPE.OK` as an error and return empty DataFrames, even when valid data is present. Replace the `elif response.resp_type == RESPONSE_TYPE.OK:` blocks to return `response.data_frame` instead of logging an error and returning an empty DataFrame.

📝 Committable Code Suggestion

‼️ Ensure you review the code suggestion before committing it to the branch. Make sure it replaces the highlighted code, contains no missing lines, and has no issues with indentation.

        elif response.resp_type == RESPONSE_TYPE.OK:
            return response.data_frame

---

Note: This comment was posted as a general PR comment because the specific line could not be resolved in the diff.

[entelligence-ai-pr-reviews]

File: mindsdb/interfaces/data_catalog/data_catalog_retriever.py (Line 179)

correctness: _construct_metadata_string_for_columns uses .iloc[0] on possibly empty DataFrame, causing IndexError if no stats row exists for a column.

🤖 AI Agent Prompt for Cursor/Windsurf

📋 Copy this prompt to your AI coding assistant (Cursor, Windsurf, etc.) to get help fixing this issue

In mindsdb/interfaces/data_catalog/data_catalog_retriever.py, line 179, `_construct_metadata_string_for_columns` uses `.iloc[0]` on a DataFrame that may be empty, which can cause an `IndexError`. Update the code to check if the filtered DataFrame is empty before accessing `.iloc[0]`, and use an empty Series if no stats row exists.

📝 Committable Code Suggestion

‼️ Ensure you review the code suggestion before committing it to the branch. Make sure it replaces the highlighted code, contains no missing lines, and has no issues with indentation.

            stats_rows = column_stats_df[column_stats_df["COLUMN_NAME"] == column_row["COLUMN_NAME"]]
            stats_row = stats_rows.iloc[0] if not stats_rows.empty else pd.Series()

---

Note: This comment was posted as a general PR comment because the specific line could not be resolved in the diff.

[ZoranPandovski]

I've added few minor comments

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (3)

Skipped posting 3 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (3)

59-152: _make_table_response is a large, highly complex function (22 branches, 61 statements) that is difficult to maintain and optimize, increasing risk of performance and correctness bugs.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 4/5
• Urgency Impact: 2/5
• Total Score: 8/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the `_make_table_response` function in mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (lines 59-152). The function is overly complex (22 branches, 61 statements), making it hard to maintain and optimize. Break it into smaller, well-named helper functions for each major code path (e.g., ODBC vs pymssql, type inference, DataFrame construction). Ensure the refactor preserves all current logic and performance, but results in a function with much lower cyclomatic complexity and improved readability.

---

430-448: table_name is interpolated directly into SQL in get_columns, allowing SQL injection if untrusted input is passed.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 4/5
• Total Score: 11/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, lines 430-448, the `get_columns` method constructs a SQL query by directly interpolating `table_name` into the query string, which allows SQL injection if untrusted input is passed. Refactor this code to use a parameterized query instead of string interpolation. Update the call to `self.native_query` to accept query parameters, and ensure the rest of the code supports this change.

---

464-491: Direct string interpolation of table_names in meta_get_tables enables SQL injection if untrusted table names are supplied.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 3/5
• Urgency Impact: 4/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, lines 464-491, the `meta_get_tables` method interpolates `table_names` directly into the SQL query, which is vulnerable to SQL injection. Refactor this to use parameterized queries (with placeholders) and pass the table names as parameters to the query execution function. Ensure the rest of the code supports passing parameters.

---

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (1)

Skipped posting 1 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/access_handler/access_handler.py (1)

116-119: query and native_query methods directly interpolate user-supplied SQL into cursor.execute(query), enabling SQL injection attacks that can read, modify, or destroy database contents.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 4/5
• Total Score: 11/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/access_handler/access_handler.py, lines 116-119, the `native_query` method executes user-supplied SQL directly via `cursor.execute(query)`, making it vulnerable to SQL injection. Refactor this code to require parameterized queries (e.g., pass a tuple of (query_string, parameters)), and reject or raise an error if a raw string is provided. Ensure all SQL execution uses parameterized statements to eliminate injection risk.

---

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (3)

Skipped posting 3 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/access_handler/access_handler.py (3)

94-94: Inconsistent error message refers to 'SQLite' instead of 'Access' in connection error logging, which can mislead debugging and monitoring.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 9/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/access_handler/access_handler.py, line 94, the error message incorrectly refers to 'SQLite' instead of 'Access' when logging connection errors. Update the message to refer to 'Access' to accurately reflect the handler and avoid confusion during debugging.

---

118-119: The native_query method fetches all results into memory with cursor.fetchall(), which can cause high memory usage or crashes for large result sets.

📊 Impact Scores:

• Production Impact: 4/5
• Fix Specificity: 2/5
• Urgency Impact: 3/5
• Total Score: 9/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/access_handler/access_handler.py, lines 118-119, the code uses `cursor.fetchall()` to load all query results into memory at once, which can cause high memory usage or crashes for large result sets. Refactor this to fetch results in batches (e.g., using `fetchmany(10000)`) and accumulate them, to handle large datasets efficiently.

---

104-143: native_query executes raw SQL queries from untrusted input without sanitization, enabling SQL injection and full database compromise if attacker controls query.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 4/5
• Total Score: 11/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/access_handler/access_handler.py, lines 104-143, the `native_query` method executes raw SQL queries from the `query` parameter without any sanitization, making it vulnerable to SQL injection if untrusted input is passed. Add a check at the start of the method to block queries containing dangerous SQL meta-characters (e.g., ';--', 'drop ', 'delete ', etc.) and return an error response if detected. This will prevent attackers from injecting malicious SQL. Do not remove the method, just add the input validation as shown in the suggestion.

---

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (4)

Skipped posting 4 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/mssql_handler/__init__.py (1)

17-17: type = HANDLER_TYPE.DATA shadows the Python built-in type, which can cause unexpected runtime errors if type is used later as a function.

📊 Impact Scores:

• Production Impact: 3/5
• Fix Specificity: 5/5
• Urgency Impact: 2/5
• Total Score: 10/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/__init__.py, line 17, the variable `type` is assigned but this shadows the Python built-in `type`, which can cause runtime errors if `type` is used as a function later. Please rename this variable to `handler_type` and update all references in this file accordingly.

---

mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (3)

59-151: _make_table_response is a large, highly complex function (22 branches, 61 statements) that is difficult to maintain and optimize, increasing risk of performance and maintainability issues as data and logic grow.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 4/5
• Urgency Impact: 2/5
• Total Score: 8/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the function `_make_table_response` in mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (lines 59-151). The function is too large and complex (22 branches, 61 statements), making it hard to maintain and optimize. Extract the MySQL type inference logic into a separate helper function (e.g., `_infer_mysql_types`) and keep the main function focused on DataFrame construction and response assembly. Ensure the refactor preserves all logic and exact formatting.

---

447-447: Direct string interpolation of table_name in SQL queries allows SQL injection if table_name is user-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 4/5
• Total Score: 11/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 447, the code interpolates `table_name` directly into a SQL query, which is vulnerable to SQL injection. Add validation to ensure `table_name` is a safe identifier before using it in the query. If invalid, raise an exception.

---

661-661: Direct string interpolation of table_names in SQL queries allows SQL injection if any table name is user-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 5/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 661, the code interpolates table names from `table_names` directly into a SQL query, which is vulnerable to SQL injection. Add validation to ensure each table name is a safe identifier before using it in the query. If any are invalid, raise an exception.

---

[entelligence-ai-pr-reviews]

Review Summary

🏷️ Draft Comments (6)

Skipped posting 6 draft comments that were valid but scored below your review threshold (>=13/15). Feel free to update them here.

mindsdb/integrations/handlers/mssql_handler/__init__.py (1)

17-17: type is used as a variable name, shadowing the Python built-in type, which can cause unexpected runtime errors if the built-in is needed elsewhere in the module.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 3/5
• Urgency Impact: 2/5
• Total Score: 7/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/__init__.py, line 17, the variable `type` is used, which shadows the Python built-in `type`. Rename this variable to `handler_type` throughout the file to avoid potential runtime errors and maintain clarity.

---

mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (5)

59-152: _make_table_response is a large, highly complex function (22 branches, 61 statements) that is difficult to maintain and optimize, increasing risk of performance and maintainability issues as data and logic grow.

📊 Impact Scores:

• Production Impact: 2/5
• Fix Specificity: 4/5
• Urgency Impact: 2/5
• Total Score: 8/15

🤖 AI Agent Prompt (Copy & Paste Ready):

Refactor the function `_make_table_response` in mindsdb/integrations/handlers/mssql_handler/mssql_handler.py (lines 59-152) to reduce its cyclomatic complexity and improve maintainability. Extract the MySQL type inference logic into a separate helper function (e.g., `_infer_mysql_types`). Ensure the refactored code preserves all original logic and formatting, and that the main function is significantly shorter and easier to maintain.

---

447-447: Direct string interpolation of table_name in SQL (e.g., table_name = '{table_name}') allows SQL injection if table_name is attacker-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 5/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 447, the code interpolates `table_name` directly into the SQL query, which allows SQL injection if `table_name` is attacker-controlled. Add a type and format check to ensure `table_name` is a valid identifier before using it in the query. If invalid, raise a ValueError.

---

491-491: Direct string interpolation of table_names into SQL (e.g., IN ({','.join(quoted_names)})) allows SQL injection if any table name is attacker-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 5/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 491, the code interpolates `table_names` directly into the SQL query, which allows SQL injection if any table name is attacker-controlled. Add a type and format check to ensure all table names are valid identifiers before using them in the query. If any are invalid, raise a ValueError.

---

530-530: Direct string interpolation of table_names into SQL (e.g., IN ({','.join(quoted_names)})) in meta_get_columns allows SQL injection if any table name is attacker-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 5/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 530, the code interpolates `table_names` directly into the SQL query, which allows SQL injection if any table name is attacker-controlled. Add a type and format check to ensure all table names are valid identifiers before using them in the query. If any are invalid, raise a ValueError.

---

625-625: Direct string interpolation of table_names into SQL (e.g., IN ({','.join(quoted_names)})) in meta_get_primary_keys allows SQL injection if any table name is attacker-controlled.

📊 Impact Scores:

• Production Impact: 5/5
• Fix Specificity: 2/5
• Urgency Impact: 5/5
• Total Score: 12/15

🤖 AI Agent Prompt (Copy & Paste Ready):

In mindsdb/integrations/handlers/mssql_handler/mssql_handler.py, line 625, the code interpolates `table_names` directly into the SQL query, which allows SQL injection if any table name is attacker-controlled. Add a type and format check to ensure all table names are valid identifiers before using them in the query. If any are invalid, raise a ValueError.

---