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DryRun Security SummaryThe pull request introduces intentional security vulnerabilities across multiple files and technologies, including hardcoded credentials, SQL injection, unsafe deserialization, excessive permissions, and remote code execution risks in Java, Python, JavaScript, and Kubernetes configurations. Expand for full summaryThis PR introduces multiple security-critical changes across various files in an intentionally vulnerable application demonstrating numerous security risks. The changes include hardcoded credentials, SQL injection vulnerabilities, unsafe deserialization, excessive permissions, and potential remote code execution points across multiple technologies including Java, Python, JavaScript, and Kubernetes configurations. Critical Security Vulnerabilities:
Code AnalysisWe ran
Overall Riskiness🔴 Risk threshold exceeded. |
![semgrep-code-latiotech[bot]](https://avatars.githubusercontent.com/u/104798912?s=60&v=4){kind=small}
| asyncTasks.push( | ||
| (async () => { | ||
| try { | ||
| const compiled = _.template(postData.template); |
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Risk: lodash 4.17.x before 4.17.12, lodash.defaultsdeep 4.6.x before 4.6.1, lodash.mergewith 4.6.x before 4.6.2, lodash.merge 4.6.x before 4.6.2, and lodash.template 4.5.x before 4.5.0 are vulnerable to improper input validation. Several lodash methods unsafely perform object merges, allowing user input to override object prototypes. Upgrade to lodash 4.17.12, lodash.defaultsdeep 4.6.1, lodash.mergewith 4.6.2, lodash.merge 4.6.2, or lodash.template 4.5.0.
Fix: Upgrade this library to at least version 4.17.12 at insecure-kubernetes-deployments/insecure-js/package-lock.json:1087.
Reference(s): GHSA-jf85-cpcp-j695, CVE-2019-10744
💬 To ignore this, reply with:
• /fp <comment> for false positive
• /ar <comment> for acceptable risk
• /other <comment> for all other reasons
Alternatively, triage in Semgrep AppSec Platform to ignore the finding created by ssc-a4fd0e64-ce76-449c-8e09-743db9edce4e.
| asyncTasks.push( | ||
| (async () => { | ||
| try { | ||
| const parsedObject = JSON5.parse(postData.json5data); |
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Risk: The parse method of the JSON5 library before and including version 2.2.1 does not restrict parsing of keys named proto, allowing specially crafted strings to pollute the prototype of the resulting object. This vulnerability pollutes the prototype of the object returned by JSON5.parse and not the global Object prototype, which is the commonly understood definition of Prototype Pollution. However, polluting the prototype of a single object can have significant security impact for an application if the object is later used in trusted operations.
Fix: Upgrade this library to at least version 2.2.2 at insecure-kubernetes-deployments/insecure-js/package-lock.json:1076.
Reference(s): GHSA-9c47-m6qq-7p4h, CVE-2022-46175
💬 To ignore this, reply with:
• /fp <comment> for false positive
• /ar <comment> for acceptable risk
• /other <comment> for all other reasons
Alternatively, triage in Semgrep AppSec Platform to ignore the finding created by ssc-9573292b-0077-44a6-b92f-3111f0ffbaca.
| asyncTasks.push( | ||
| (async () => { | ||
| try { | ||
| const compiled = _.template(postData.template); |
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Risk: lodash versions prior to 4.17.21 (or lodash.template version 4.6.2) are vulnerable to Command Injection via the template function. Please remediate by updating to version 4.17.21 (or 4.6.2). GHSA-35jh-r3h4-6jhm
Fix: Upgrade this library to at least version 4.17.21 at insecure-kubernetes-deployments/insecure-js/package-lock.json:1087.
Reference(s): GHSA-35jh-r3h4-6jhm, CVE-2021-23337
💬 To ignore this, reply with:
• /fp <comment> for false positive
• /ar <comment> for acceptable risk
• /other <comment> for all other reasons
Alternatively, triage in Semgrep AppSec Platform to ignore the finding created by ssc-c6bc1896-7044-4b22-b31a-753d52070423.
![jit-ci[bot]](https://avatars.githubusercontent.com/in/142441?s=60&v=4){kind=small}
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❌ Jit has detected 23 important findings in this PR that you should review.
The first 10 findings are detailed below as separate comments.
Click here to view all the findings on Jit.
It’s highly recommended that you fix these security issues before merging.
Alternatively, comment #jit_ignore_all in this PR to ignore all findings. Admin privileges required.
| const query = `SELECT product FROM Orders WHERE orderNumber = ${postData.orderNumber3};`; | ||
| responseMessages.push(`<p>Executing SQL query: ${query}</p>`); | ||
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| connection.query(query, (err, rows) => { |
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Security control: Static Code Analysis Js
Sql Injection Via Untrusted Function Argument In Node Mysql Queries
Detected a mysql2 SQL statement that comes from a function argument. This could lead to SQL injection if the variable is user-controlled and is not properly sanitized. In order to prevent SQL injection, it is recommended to use parameterized queries or prepared statements.
Severity: HIGH
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You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "SQL Injection via Untrusted Function Argument in Node MySQL Queries" in insecure-js/server2.js; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| (async () => { | ||
| try { | ||
| const query = `SELECT product FROM Orders WHERE orderNumber = ${postData.orderNumber};`; | ||
| const result = await sequelize.query(query, { type: sequelize.QueryTypes.SELECT }); |
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Security control: Static Code Analysis Js
Sql Injection Via Untrusted Function Argument In Node Mysql Queries
Detected a mysql2 SQL statement that comes from a function argument. This could lead to SQL injection if the variable is user-controlled and is not properly sanitized. In order to prevent SQL injection, it is recommended to use parameterized queries or prepared statements.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "SQL Injection via Untrusted Function Argument in Node MySQL Queries" in insecure-js/server2.js; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| # 2 - Command Injection | ||
| if 'command' in request.form: | ||
| cmd = request.form['command'] | ||
| process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
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Security control: Static Code Analysis Python Semgrep
User Input In Unsafe Subprocess Call In Flask
Detected user input entering a subprocess call unsafely. This could result in a command injection vulnerability. An attacker could use this vulnerability to execute arbitrary commands on the host, which allows them to download malware, scan sensitive data, or run any command they wish on the server. Do not let users choose the command to run. In general, prefer to use Python API versions of system commands. If you must use subprocess, use a dictionary to allowlist a set of commands.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "User input in unsafe subprocess call in Flask" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| cursor = conn.cursor() | ||
| try: | ||
| sql_query = f"SELECT * FROM video_games WHERE title = '{query}'" | ||
| cursor.execute(sql_query) |
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Security control: Static Code Analysis Python Semgrep
Sqlalchemy Raw Sql Query Concatenation Risks Sql Injection
Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition, use SQL Expression Language or Schema Definition Language. In most cases, SQLAlchemy ORM will be a better option.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "SQLAlchemy raw SQL query concatenation risks SQL Injection" in insecure-api/main-2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| import os | ||
| import sqlite3 | ||
| import requests | ||
| from lxml import etree |
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Security control: Static Code Analysis Python Semgrep
Potential Xxe Vulnerability With Native Python Xml Libraries
Found use of the native Python XML libraries, which is vulnerable to XML external entity (XXE)
attacks. The Python documentation recommends the 'defusedxml' library instead. Use 'defusedxml'.
See https://github.com/tiran/defusedxml for more information.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "Potential XXE vulnerability with native Python XML libraries" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| # 2 - Command Injection | ||
| if 'command' in request.form: | ||
| cmd = request.form['command'] | ||
| process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
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Security control: Static Code Analysis Python Semgrep
User Data In Subprocess Function Risks Command Injection Vulnerability
Detected subprocess function 'Popen' with user controlled data. A malicious actor could leverage this to perform command injection. You may consider using 'shlex.escape()'.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "User data in subprocess function risks command injection vulnerability" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| elif 'url' in request.form: | ||
| url = request.form['url'] | ||
| try: | ||
| response = requests.get(url) |
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Security control: Static Code Analysis Python Semgrep
Server-Side Request Forgery (Ssrf) Risk With User Data In Requests In Django
Data from request object is passed to a new server-side request. This could lead to a server-side request forgery (SSRF). To mitigate, ensure that schemes and hosts are validated against an allowlist, do not forward the response to the user, and ensure proper authentication and transport-layer security in the proxied request. See https://owasp.org/www-community/attacks/Server_Side_Request_Forgery to learn more about SSRF vulnerabilities.
Severity: HIGH
Fix suggestion:
This fix suggestion was generated by Jit. Please note that the suggestion might not always fit every use case. It is highly recommended that you check and review it before merging.
Suggestion guidelines
This remediation will modify the vulnerable request call in your code to validate the target URL before making a request. This helps prevent server-side request forgery (SSRF) attacks by ensuring that only allowed URLs are used.
| response = requests.get(url) | |
| response = requests.get(urlget.lower() in ['get', 'post'] and ensure_allowed_url() |
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "Server-Side Request Forgery (SSRF) risk with user data in requests in Django" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| try: | ||
| # Use lxml to parse the XML data | ||
| parser = etree.XMLParser(load_dtd=True, resolve_entities=True) | ||
| tree = etree.fromstring(xml_data.encode(), parser) |
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Security control: Static Code Analysis Python Semgrep
Potential Xxe Vulnerability With Native Python Xml Libraries
Found use of the native Python XML libraries, which is vulnerable to XML external entity (XXE)
attacks. The Python documentation recommends the 'defusedxml' library instead. Use 'defusedxml'.
See https://github.com/tiran/defusedxml for more information.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "Potential XXE vulnerability with native Python XML libraries" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| try: | ||
| # Vulnerable SQL query using string interpolation | ||
| query = "SELECT password FROM users WHERE username = '{}'".format(username) | ||
| cursor.execute(query) |
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Security control: Static Code Analysis Python Semgrep
Sqlalchemy Raw Sql Query Concatenation Risks Sql Injection
Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition, use SQL Expression Language or Schema Definition Language. In most cases, SQLAlchemy ORM will be a better option.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "SQLAlchemy raw SQL query concatenation risks SQL Injection" in insecure-app/app2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
| def fetch_url_content(url: str): | ||
| # Vulnerability: No validation of the URL (API10:2019 - Unsafe Consumption of APIs) | ||
| try: | ||
| response = requests.get(url) |
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Security control: Static Code Analysis Python Semgrep
Potential Ssrf With Request Data In Server-Side Requests
Data from request object is passed to a new server-side request. This could lead to a server-side request forgery (SSRF). To mitigate, ensure that schemes and hosts are validated against an allowlist, do not forward the response to the user, and ensure proper authentication and transport-layer security in the proxied request.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "Potential SSRF with request data in server-side requests" in insecure-api/main-2.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
![codacy-production[bot]](https://avatars.githubusercontent.com/in/56611?s=60&v=4){kind=small}
| image: "{{ .Values.insecureApp.image.repository }}:{{ .Values.insecureApp.image.tag }}" | ||
| env: | ||
| - name: AWS_ACCESS_KEY_ID | ||
| value: AKIA2JAPX77RGLB664VE |
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❌ Codacy found a critical Security issue: Possible hardcoded secret: AWS Access Key ID
The issue identified by the Trivy linter is that the AWS Access Key ID is hardcoded directly in the YAML configuration. Hardcoding sensitive information like AWS credentials poses a security risk, as it can be exposed in version control systems, logs, or through misconfigurations. Instead of hardcoding, it's best practice to use environment variables, secrets management tools, or Kubernetes Secrets to handle sensitive information securely.
To fix the issue, we can replace the hardcoded value with a reference to a Kubernetes Secret that stores the AWS Access Key ID. Here's the suggested change:
valueFrom:
secretKeyRef:
name: aws-credentials
key: access-key-idThis change assumes that there is a Kubernetes Secret named aws-credentials that contains the key access-key-id with the actual value of the AWS Access Key ID.
This comment was generated by an experimental AI tool.
| elif 'url' in request.form: | ||
| url = request.form['url'] | ||
| try: | ||
| response = requests.get(url) |
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The issue identified by the Bandit linter is that the requests.get(url) call is made without specifying a timeout. This can lead to potential problems, such as the application hanging indefinitely if the server does not respond. By not setting a timeout, the application could become unresponsive, leading to a poor user experience or even denial of service.
To fix this issue, you can specify a timeout value in the requests.get call. Here's the code suggestion to implement this change:
| response = requests.get(url) | |
| response = requests.get(url, timeout=10) |
In this suggestion, a timeout of 10 seconds is set, which means that if the server does not respond within that time frame, a requests.exceptions.Timeout exception will be raised. Adjust the timeout value as necessary based on the expected response times of the services being called.
This comment was generated by an experimental AI tool.
| conn = sqlite3.connect('videogames.db') | ||
| cursor = conn.cursor() | ||
| try: | ||
| sql_query = f"SELECT * FROM video_games WHERE title = '{query}'" |
There was a problem hiding this comment.
The issue identified by the Bandit linter is a potential SQL injection vulnerability. This occurs because the user input (query) is directly incorporated into the SQL query string without any sanitization or parameterization. An attacker could exploit this by providing a specially crafted input that alters the intended SQL command, potentially allowing them to execute arbitrary SQL code against the database.
To fix this issue, we should use parameterized queries, which safely handle user input and prevent SQL injection attacks. Here's the single line change to implement this:
| sql_query = f"SELECT * FROM video_games WHERE title = '{query}'" | |
| cursor.execute("SELECT * FROM video_games WHERE title = ?", (query,)) |
This comment was generated by an experimental AI tool.
| username = '' | ||
| password = '' | ||
| try: | ||
| cursor.execute("SELECT * FROM users WHERE username = '%s' AND password = '%s'" % (username, password)) |
There was a problem hiding this comment.
The issue identified by the Bandit linter is a potential SQL injection vulnerability due to the use of string interpolation to construct the SQL query. By directly inserting user input into the SQL query string, an attacker could manipulate the input to execute arbitrary SQL commands, potentially compromising the database.
To fix this issue, we should use parameterized queries, which safely handle user input by separating the data from the query structure. This prevents any user input from being treated as executable SQL code.
Here's the single line change to fix the issue:
| cursor.execute("SELECT * FROM users WHERE username = '%s' AND password = '%s'" % (username, password)) | |
| cursor.execute("SELECT * FROM users WHERE username = ? AND password = ?", (username, password)) |
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| @@ -0,0 +1,168 @@ | |||
| from flask import Flask, request, render_template_string, jsonify | |||
| import subprocess | |||
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The issue with importing the subprocess module is that it can lead to security vulnerabilities, especially if user input is passed directly to the subprocess commands. This can result in command injection attacks, where an attacker could execute arbitrary commands on the server. To mitigate this risk, you should avoid using subprocess unless absolutely necessary, and if you do use it, ensure that you are properly sanitizing any user input.
If the subprocess module is not actually needed in your code, you can simply remove the import statement. Here's the suggested change:
| import subprocess | |
| # Remove the subprocess import if not needed |
This comment was generated by an experimental AI tool.
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| # Example hardcoded AWS credentials (sensitive data leakage) | ||
| aws_access_key_id = 'AKIA2JAPX77RGLB664VE' | ||
| aws_secret = 'v5xpjkWYoy45fGKFSMajSn+sqs22WI2niacX9yO5' |
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The issue identified by the Bandit linter is that the AWS secret key is hardcoded directly in the Python code. Hardcoding sensitive information such as AWS credentials in your source code can lead to security vulnerabilities, as anyone with access to the code can see these credentials. This practice can lead to unauthorized access to your AWS resources, data breaches, and other security risks.
To fix this issue, it's recommended to use environment variables to securely store sensitive information. This way, the credentials are not exposed in the source code.
Here's the suggested change to implement this fix:
| aws_secret = 'v5xpjkWYoy45fGKFSMajSn+sqs22WI2niacX9yO5' | |
| aws_secret = os.getenv('AWS_SECRET_KEY') |
Make sure to set the AWS_SECRET_KEY environment variable in your operating system or deployment environment before running the application.
This comment was generated by an experimental AI tool.
| username = request.form['username'] | ||
| try: | ||
| # Vulnerable SQL query using string interpolation | ||
| query = "SELECT password FROM users WHERE username = '{}'".format(username) |
There was a problem hiding this comment.
The issue identified by the Bandit linter is that the SQL query is constructed using string interpolation, which makes it vulnerable to SQL injection attacks. An attacker could manipulate the username input to execute arbitrary SQL commands, potentially compromising the database.
To fix this issue, you should use parameterized queries, which safely handle user inputs by separating SQL logic from data. This prevents the possibility of SQL injection.
Here's the suggested single line change:
query = "SELECT password FROM users WHERE username = %s"
cursor.execute(query, (username,))This comment was generated by an experimental AI tool.
| # 2 - Command Injection | ||
| if 'command' in request.form: | ||
| cmd = request.form['command'] | ||
| process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
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❌ Codacy found a critical Security issue: subprocess call with shell=True identified, security issue.
The issue identified by Bandit is related to the use of subprocess.Popen with shell=True. This can lead to security vulnerabilities, specifically command injection attacks, where an attacker can execute arbitrary commands on the server by manipulating the input to the cmd variable. By allowing shell execution, the application becomes vulnerable to executing unintended commands.
To mitigate this risk, it's recommended to avoid using shell=True and instead pass the command and its arguments as a list. This approach prevents the shell from interpreting the command and reduces the risk of injection.
Here’s the suggested code change:
| process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
This change ensures that cmd is split into a list of arguments, which subprocess.Popen will handle safely without invoking the shell.
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| def fetch_url_content(url: str): | ||
| # Vulnerability: No validation of the URL (API10:2019 - Unsafe Consumption of APIs) | ||
| try: | ||
| response = requests.get(url) |
There was a problem hiding this comment.
The issue identified by the Bandit linter is that the requests.get(url) call does not specify a timeout. This can lead to the application hanging indefinitely if the external server does not respond, potentially leading to denial of service (DoS) vulnerabilities. It's a best practice to always set a timeout when making network requests to ensure that your application can handle unresponsive servers gracefully.
To fix this issue, you can specify a timeout in the requests.get call. Here's the code suggestion:
| response = requests.get(url) | |
| response = requests.get(url, timeout=5) |
This comment was generated by an experimental AI tool.
| """, output=output) | ||
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| if __name__ == '__main__': | ||
| app.run(host='0.0.0.0', port=8080, debug=True) |
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❌ Codacy found a critical Security issue: A Flask app appears to be run with debug=True, which exposes the Werkzeug debugger and allows the execution of arbitrary code.
The issue identified by the Bandit linter is that running a Flask application with debug=True exposes the Werkzeug debugger. This can be a significant security risk, as it allows an attacker to execute arbitrary code on the server if they can trigger an error in the application. In a production environment, it's crucial to disable the debugger to prevent potential exploitation.
To fix this issue, you should set debug=False when running the application. This will ensure that the debugger is not accessible, thus enhancing the security of your application.
Here is the code suggestion to address the issue:
| app.run(host='0.0.0.0', port=8080, debug=True) | |
| app.run(host='0.0.0.0', port=8080, debug=False) |
This comment was generated by an experimental AI tool.
| try: | ||
| # Use lxml to parse the XML data | ||
| parser = etree.XMLParser(load_dtd=True, resolve_entities=True) | ||
| tree = etree.fromstring(xml_data.encode(), parser) |
There was a problem hiding this comment.
The issue identified by the Bandit linter is that using lxml.etree.fromstring to parse untrusted XML data can lead to various XML attacks, such as XML External Entity (XXE) attacks. This vulnerability arises because the parser can be tricked into processing external entities, which could lead to unauthorized access to local files or other sensitive information. To mitigate this risk, it's recommended to use the defusedxml library, which is specifically designed to safely parse untrusted XML data.
To fix the issue, you should replace the lxml.etree.fromstring function with the equivalent function from the defusedxml package. Here's the single line change to address the vulnerability:
| tree = etree.fromstring(xml_data.encode(), parser) | |
| tree = defusedxml.ElementTree.fromstring(xml_data.encode()) |
This comment was generated by an experimental AI tool.
| import os | ||
| import sqlite3 | ||
| import requests | ||
| from lxml import etree |
There was a problem hiding this comment.
The issue identified by the Bandit linter pertains to the use of lxml.etree for parsing XML data. The etree module from the lxml library can be vulnerable to various XML-related attacks, such as XML External Entity (XXE) attacks, if it processes untrusted XML input. This can lead to security vulnerabilities, including the potential for data exfiltration or denial of service.
To mitigate this risk, it is recommended to use the defusedxml package, which is designed to safely parse XML without these vulnerabilities.
To fix the issue, you can replace the import statement for lxml.etree with the equivalent import from defusedxml. Here is the code suggestion:
| from lxml import etree | |
| from defusedxml.lxml import etree |
This comment was generated by an experimental AI tool.
| db = sqlite3.connect("tutorial.db") | ||
| cursor = db.cursor() | ||
| username = '' | ||
| password = '' |
There was a problem hiding this comment.
The issue highlighted by the Bandit linter is that the variable password is hardcoded with an empty string. Hardcoding sensitive information like passwords is a security risk because it can lead to unauthorized access if the code is exposed. Instead of hardcoding, passwords should be securely managed, for instance, by retrieving them from environment variables or a secure secrets management service.
To fix this issue, you can modify the code to retrieve the password from a more secure source. Here's a suggestion to replace the hardcoded password:
| password = '' | |
| password = os.getenv('USER_PASSWORD', '') |
This change retrieves the password from an environment variable named USER_PASSWORD, and defaults to an empty string if the variable is not set.
This comment was generated by an experimental AI tool.
| """, output=output) | ||
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| if __name__ == '__main__': | ||
| app.run(host='0.0.0.0', port=8080, debug=True) |
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The issue identified by the Bandit linter is that binding the application to 0.0.0.0 allows it to be accessible from any network interface. This can expose the application to potential security risks, especially if it is running in a production environment. It is generally recommended to bind to 127.0.0.1 (localhost) during development or to a specific interface in production to limit exposure.
To fix this issue, you can change the host parameter in the app.run() method to bind only to localhost.
| app.run(host='0.0.0.0', port=8080, debug=True) | |
| app.run(host='127.0.0.1', port=8080, debug=True) |
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| password = '' | ||
| try: | ||
| cursor.execute("SELECT * FROM users WHERE username = '%s' AND password = '%s'" % (username, password)) | ||
| except: |
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The issue with the line except: is that it catches all exceptions without handling them properly. This can lead to silent failures, making it difficult to debug issues because the code does not provide any feedback or logging when an error occurs. It's generally a bad practice to use a bare except as it can mask unexpected errors and make the code less maintainable.
To fix this, we should at least log the exception or handle it in a way that provides feedback. A common approach is to catch specific exceptions or log the error.
Here's a single line code suggestion to improve the error handling by logging the exception:
| except: | |
| except Exception as e: print(f"Error: {e}") |
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| password = '' | ||
| try: | ||
| cursor.execute("SELECT * FROM users WHERE username = '%s' AND password = '%s'" % (username, password)) | ||
| except: |
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ℹ️ Codacy found a minor CodeStyle issue: do not use bare 'except' (E722)
The issue with using a bare except: is that it catches all exceptions, including system-exiting exceptions like KeyboardInterrupt and SystemExit, which can make debugging difficult and lead to unintended behavior. It's generally better to catch specific exceptions that you expect might occur.
To fix the issue, you can change the bare except: to catch a more specific exception, such as sqlite3.Error, which is appropriate for database-related errors.
Here’s the suggested code change:
| except: | |
| except sqlite3.Error: |
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| # Example hardcoded AWS credentials (sensitive data leakage) | ||
| aws_access_key_id = 'AKIA2JAPX77RGLB664VE' | ||
| aws_secret = 'v5xpjkWYoy45fGKFSMajSn+sqs22WI2niacX9yO5' |
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❌ Codacy found a critical Security issue: Amazon Web Services secret key (aws_secret_key)
The issue identified by the Prospector linter is that hardcoding sensitive information, such as AWS credentials, directly in the source code can lead to security vulnerabilities. If the code is shared or deployed, these credentials can be exposed, allowing unauthorized access to AWS resources. It is crucial to manage sensitive data securely, typically by using environment variables or secure vaults.
To fix this issue, you should remove the hardcoded AWS secret and instead retrieve it from an environment variable. Here’s the suggested change:
| aws_secret = 'v5xpjkWYoy45fGKFSMajSn+sqs22WI2niacX9yO5' | |
| aws_secret = os.environ.get('AWS_SECRET_ACCESS_KEY') |
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| @app.post("/feedback") | ||
| def submit_feedback(feedback: str): | ||
| # Vulnerability: User input is not sanitized before rendering (API7:2019 - Security Misconfiguration) | ||
| response = HTMLResponse(content=f"<html><body><h1>Feedback Received</h1><p>{feedback}</p></body></html>") |
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ℹ️ Codacy found a minor CodeStyle issue: undefined name 'HTMLResponse' (F821)
The issue identified by the Prospector linter is that the name HTMLResponse is undefined in the current scope. This typically means that the necessary import statement for HTMLResponse is missing from the file. To resolve this, you need to import HTMLResponse from the appropriate module, which is usually fastapi.responses.
Here’s the suggested code change to fix the issue by adding the necessary import statement:
| response = HTMLResponse(content=f"<html><body><h1>Feedback Received</h1><p>{feedback}</p></body></html>") | |
| from fastapi.responses import HTMLResponse |
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| @@ -0,0 +1,168 @@ | |||
| from flask import Flask, request, render_template_string, jsonify | |||
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ℹ️ Codacy found a minor CodeStyle issue: 'flask.jsonify' imported but unused (F401)
The issue reported by the Prospector linter indicates that the jsonify function imported from the flask module is not being used anywhere in the code. This is a code style issue because unused imports can lead to unnecessary clutter and may confuse other developers who read the code.
To fix the issue, you can simply remove the unused import statement for jsonify. Here is the code suggestion to address the issue:
| from flask import Flask, request, render_template_string, jsonify | |
| from flask import Flask, request, render_template_string |
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| app = Flask(__name__) | ||
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| @app.route('/', methods=['GET', 'POST']) | ||
| def index(): |
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ℹ️ Codacy found a minor CodeStyle issue: index is too complex (22) (MC0001)
The issue described by the Prospector linter indicates that the index function is too complex, specifically with a complexity score of 22. This typically means that the function has too many branches, loops, or other control flow structures, making it harder to read and maintain. To address this, it's a good practice to refactor the code into smaller, more manageable functions.
To simplify the index function, we can extract the database query logic into a separate function. Here’s a single line change that encapsulates the database interaction:
| def index(): | |
| def index(): return handle_user_authentication() |
This change suggests that we create a new function called handle_user_authentication() that would contain the logic for handling the user authentication process, thus reducing the complexity of the index function.
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| @@ -0,0 +1,214 @@ | |||
| from fastapi import FastAPI, HTTPException, Header, Request | |||
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ℹ️ Codacy found a minor CodeStyle issue: 'fastapi.Request' imported but unused (F401)
The issue reported by the Prospector linter indicates that the Request class from the FastAPI module has been imported but is not being used anywhere in the provided code fragment. This can lead to unnecessary imports, making the code less clean and potentially confusing for other developers.
To fix this issue, you can remove the unused import of Request. Here’s the single line change:
| from fastapi import FastAPI, HTTPException, Header, Request | |
| from fastapi import FastAPI, HTTPException, Header |
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| <pre>{{ output|safe }}</pre> | ||
| """, output=output) | ||
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| if __name__ == '__main__': |
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ℹ️ Codacy found a minor CodeStyle issue: expected 2 blank lines after class or function definition, found 1 (E305)
The issue identified by the Prospector linter is related to PEP 8, which is the style guide for Python code. According to PEP 8, there should be two blank lines before the definition of a class or function, including the if __name__ == '__main__': block. In your code, there is only one blank line before this block, which is why the linter is raising the E305 issue.
To fix this issue, you need to add an additional blank line before the if __name__ == '__main__': line.
Here’s the code suggestion to address the issue:
<code suggestion>
<code suggestion>
if __name__ == '__main__':This comment was generated by an experimental AI tool.
| @@ -0,0 +1,214 @@ | |||
| from fastapi import FastAPI, HTTPException, Header, Request | |||
| from typing import Optional | |||
| from models import VideoGame, User | |||
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ℹ️ Codacy found a minor CodeStyle issue: 'models.User' imported but unused (F401)
The issue reported by the Prospector linter indicates that the User class imported from the models module is not being used anywhere in the code fragment. This is considered a code style issue because unused imports can clutter the code and make it less readable.
To fix this issue, you can remove the unused import. Since the User class is not utilized in the provided code fragment, you can modify the import statement to only include VideoGame. Here’s the single line change to address the issue:
| from models import VideoGame, User | |
| from models import VideoGame |
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| .join('')}</ul></p>` | ||
| : `<p>No users found</p>`; | ||
| } catch (error) { | ||
| responseMessages[index] += `<p>Sequelize findAll error: ${error.message}</p>`; |
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The issue identified by the ESLint linter, specifically the "Generic Object Injection Sink," refers to the potential security risk of directly including user-controlled data (in this case, error.message) in the output without proper sanitization. This can lead to vulnerabilities such as Cross-Site Scripting (XSS) if the error message contains malicious content.
To mitigate this risk, it's important to sanitize or escape any user-controlled inputs before including them in the response. A common approach is to use a library or utility function that escapes HTML characters.
Here’s a code suggestion that uses a hypothetical escapeHtml function to sanitize the error message:
| responseMessages[index] += `<p>Sequelize findAll error: ${error.message}</p>`; | |
| responseMessages[index] += `<p>Sequelize findAll error: ${escapeHtml(error.message)}</p>`; |
Make sure to implement or import the escapeHtml function to properly escape any HTML special characters in the error message.
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| if (err) { | ||
| responseMessages[index] += `<p>SQLite error: ${err.message}</p>`; | ||
| } else { | ||
| responseMessages[index] += rows.length > 0 |
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The issue highlighted by ESLint regarding "Generic Object Injection Sink" pertains to the potential for an attacker to manipulate the postData.orderNumber2 input, which can lead to an injection attack. Since the input is directly used in the SQL query without proper sanitization or parameterization, it could allow malicious SQL code to be executed.
To mitigate this risk, it's essential to use parameterized queries or prepared statements. In the case of SQLite, you can use placeholders for the parameters in the SQL query to ensure that the input is properly escaped and not treated as executable code.
Here's the suggested code change to fix the issue by using a parameterized query:
const query = `SELECT product FROM Orders WHERE orderNumber = ?;`;
db.all(query, [postData.orderNumber2], (err, rows) => {This comment was generated by an experimental AI tool.
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❌ Jit has detected 24 important findings in this PR that you should review.
The first 10 findings are detailed below as separate comments.
Click here to view all the findings on Jit.
It’s highly recommended that you fix these security issues before merging.
Alternatively, comment #jit_ignore_all in this PR to ignore all findings. Admin privileges required.
| cursor = conn.cursor() | ||
| try: | ||
| sql_query = f"SELECT * FROM tiles WHERE title = '{query}'" | ||
| cursor.execute(sql_query) |
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Security control: Static Code Analysis Python Semgrep
Sqlalchemy Raw Sql Query Concatenation Risks Sql Injection
Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition, use SQL Expression Language or Schema Definition Language. In most cases, SQLAlchemy ORM will be a better option.
Severity: HIGH
Jit Bot commands and options (e.g., ignore issue)
You can trigger Jit actions by commenting on this PR review:
#jit_ignore_fpIgnore and mark this specific single instance of finding as “False Positive”#jit_ignore_acceptIgnore and mark this specific single instance of finding as “Accept Risk”#jit_ignore_type_in_fileIgnore any finding of type "SQLAlchemy raw SQL query concatenation risks SQL Injection" in insecure-api/main.py; future occurrences will also be ignored.#jit_undo_ignoreUndo ignore command
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