GHSA-xxh2-68g9-8jqr ormar is vulnerable to SQL Injection through aggregate functions min() and max()

• PyPI ormar: >= 0.9.9, < 0.23.0 → fixed in 0.23.0

Report of SQL Injection Vulnerability in Ormar ORM A SQL Injection attack can be achieved by passing a crafted string to the min() or max() aggregate functions. Brief description When performing aggregate queries, Ormar ORM constructs SQL expressions by passing user-supplied column names directly into `sqlalchemy.text()` without any validation or sanitization. The `min()` and `max()` methods in the `QuerySet` class accept arbitrary string input as the column parameter. While `sum()` and `avg()` are partially protected by an `is_numeric` type check that rejects non-existent fields, `min()` and `max()` skip this validation entirely. As a result, an attacker-controlled string is embedded as raw SQL inside the aggregate function call. Any unauthorized user can exploit this vulnerability to read the entire database contents, including tables unrelated to the queried model, by injecting a subquery as the column parameter. Affected versions ``` 0.9.9 - 0.12.2 0.20.0b1 - 0.22.0 (latest) ``` The vulnerable `SelectAction.get_text_clause()` method and the `min()`/`max()` aggregate functions were introduced together in commit `ff9d412` (March 12, 2021) and first released in version 0.9.9. The vulnerable code has never been modified since — `get_text_clause()` is identical in every subsequent version through the latest 0.21.0. Versions prior to 0.9.9 do not contain the `min()`/`max()` aggregate feature and are not affected. The following uses the latest ormar 0.21.0 as an example to illustrate the attack. Vulnerability details When performing an aggregate query, the `QuerySet.max()` method (line 721, `queryset.py`) passes user input to `_query_aggr_function()`. This method creates a `SelectAction` object for each column name. The column string is split by `__` and the last part becomes `self.field_name` — with no validation against the model's actual fields. The critical vulnerability is in `SelectAction.get_text_clause()` (line 41-43, `select_action.py`), which directly passes `self.field_name` into `sqlalchemy.text()`: ```python select_action.py line 41-43 def get_text_clause(self) -> sqlalchemy.sql.expression.TextClause: alias = f"{self.table_prefix}_" if self.table_prefix else "" return sqlalchemy.text(f"{alias}{self.field_name}") # unsanitised user input! ``` The `apply_func()` method then wraps this raw text clause inside `func.max()`, producing SQL like `max()`. Since `sqlalchemy.text()` treats its argument as literal SQL, any subquery or SQL expression injected through the column name will be executed by the database engine. The `_query_aggr_function()` method (line 704-719, `queryset.py`) only validates field types for `sum` and `avg`, leaving `min` and `max` completely unprotected: ```python queryset.py line 704-719 async def _query_aggr_function(self, func_name: str, columns: List) -> Any: func = getattr(sqlalchemy.func, func_name) select_actions = [ SelectAction(select_str=column, model_cls=self.model) for column in columns ] if func_name in ["sum", "avg"]: # 1 else result[0] ``` To reproduce the attack, you can follow the steps below, using a FastAPI application with SQLite as an example. Note: The PoC consists of two files provided in the attachments — `poc_server.py` (the vulnerable server) and `poc_attacker.py` (the HTTP-based attacker script). Start the vulnerable application Install dependencies: pip install ormar databases aiosqlite fastapi uvicorn httpx The vulnerable server (poc_server.py) is based on the official ormar FastAPI example (ormar/examples/fastapi_quick_start.py). The only modification is the addition of a /items/stats endpoint — a common pattern for applications that provide aggregate statistics. This demonstrates that the vulnerability is easily triggered by natural API design. The server defines three models: Category and Item — from the official ormar example (unchanged) AdminUser — simulates internal data (e.g., an admin_users table) that should NOT be accessible through the public API The vulnerable endpoint: # Added endpoint: aggregate statistics (VULNERABLE) This is a common and natural pattern — letting users request statistics on different columns. The ormar documentation itself shows: await Book.objects.max(columns=[&quot;year&quot;]) See: &lt;https://collerek.github.io/ormar/queries/aggregations/&gt; @app.get(&quot;/items/stats&quot;) async def item_stats( metric: str = Query(&quot;max&quot;, description=&quot;max or min&quot;), column: str = Query(&quot;price&quot;, description=&quot;Column to aggregate&quot;), ): &quot;&quot;&quot;Return aggregate statistics for items.&quot;&quot;&quot; if metric == &quot;max&quot;: result = await Item.objects.max(column) elif metric == &quot;min&quot;: result = await Item.objects.min(column) else: return {&quot;error&quot;: &quot;Unsupported metric&quot;} return {&quot;metric&quot;: metric, &quot;column&quot;: column, &quot;result&quot;: result} The database contains: Table | Data -- | -- categories | Electronics items | Laptop ($999.99), Phone ($699.99), Tablet ($449.99), Monitor ($329.99) admin_users | root / Sup3r$ecretP@ss! / ak-9f8e7d6c5b4a3210-prod   | deploy-bot / ghp_Tx7KmR29vLp4QzN1bWcA3sYjDf80Ue5Xoi / ak-1a2b3c4d5e6f7890-ci The admin_users table is NOT exposed via any API endpoint. The attack steps The PoC requires two terminals: Terminal 1 — Start the vulnerable server: python poc_server.py Terminal 2 — Run the attacker script: python poc_attacker.py The attacker script (poc_attacker.py) sends HTTP requests to the running server. It has NO prior knowledge of the database schema — all information is discovered through the injection. The attacker executes 6 progressive attack stages through the single /items/stats endpoint. Principle of vulnerability exploitation 1. The attacker confirms injection by sending an arithmetic expression The attacker sends GET /items/stats?metric=max&amp;column=1+1. The data flow is: HTTP request: GET /items/stats?metric=max&amp;column=1+1 ↓ item_stats(metric=&quot;max&quot;, column=&quot;1+1&quot;) # poc_server.py ↓ Item.objects.max(&quot;1+1&quot;) # queryset.py:721 ↓ _query_aggr_function(func_name=&quot;max&quot;, columns=[&quot;1+1&quot;]) # queryset.py:704 ↓ SelectAction(select_str=&quot;1+1&quot;, model_cls=Item) # select_action.py:22 ↓ _split_value_into_parts(&quot;1+1&quot;) → self.field_name = &quot;1+1&quot; ↓ min/max skip the is_numeric check (line 709 only checks sum/avg) ↓ get_text_clause() → sqlalchemy.text(&quot;1+1&quot;) # select_action.py:43 ↓ apply_func(sqlalchemy.func.max) → max(1+1) Generated SQL: SELECT max(1+1) AS &quot;1+1&quot; FROM (SELECT i…