Blog Article

LLM Threat Vector: Supply-Chain Vulnerabilities

Arnav Bathla

8 min read

The Double-Edged Sword of AI-Generated Code

AI-powered dev tools have massively increased productivity and creativity in the development process, offering unprecedented speed in generating code. However, this convenience sometimes comes with the risk of inadvertently introducing security vulnerabilities. It's akin to walking a tightrope where balance between efficiency and security is key.

Given the amount of code online, there’s a high chance that SOTA models can spit out malicious code for certain user prompts.This is merely possible because SOTA models are trained on enormous amounts of data that is freely available online. This can poison the model that is eventually used in AI-powered dev tools.

Here's a screenshot of the aforementioned concept from a paper, "Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code" by Cristina Improta:

Vulnerability Example

Consider a scenario where an AI tool generates a piece of code for handling user-uploaded files in a web application. At first glance, the code seems efficient, but it hides a critical vulnerability.

Initial AI-Generated Code Example:

import sqlite3

// Connect to the database
conn = sqlite3.connect('example.db')
c = conn.cursor()

// Function to query the database based on user_id
def get_user_details(user_id):
    // Vulnerable SQL query
    query = "SELECT * FROM users WHERE id = '" + user_id + "';"
    return c.fetchall()

// Example of user input that could exploit the vulnerability
user_input = "1'; DROP TABLE users; --"
// Call the function with unsafe user input

// Close the connection

In the above script, the get_user_details function constructs a SQL query by directly appending the user_id variable to the query string. If an attacker provides a user_id in the form of a SQL statement like "1'; DROP TABLE users; --", it would result in the execution of the unintended command (in this case, dropping the users table), thereby exploiting the SQL injection vulnerability.

Other Example Attack Scenarios:
  1. Code Suggestion Backdoor Insertion: An attacker infiltrates an AI-powered code completion tool's training data with snippets containing a backdoor. As developers use the tool, it suggests these compromised snippets, which, if included in the codebase, grant the attacker unauthorized access to the application once deployed.

  2. Library Upgrade Attack: An attacker pushes a malicious update to a popular library used by developers. An AI-powered dependency update tool, designed to suggest and automate library updates, incorporates the compromised library version into countless projects without the developers realizing it.

  3. Model Interpretability Misdirection: An AI-powered model interpretability tool is tampered with to hide certain behaviors of machine learning models. This could lead to developers overlooking malicious functionality within the models, such as embedded biases or triggers that cause the model to behave unpredictably under specific circumstances.

  4. Auto-Pull Request Merge: An attacker exploits a bot that uses AI to automate the merging of pull requests based on passing test cases and code quality checks. The attacker crafts a pull request that passes all automated checks but includes malicious code, which the bot then automatically merges into the codebase.

  5. Auto-Documentation Manipulation: An AI-driven tool that automatically generates code documentation is compromised. The documentation includes subtle, intentionally inserted inaccuracies that lead to misinterpretation of the code's functionality, potentially causing developers to introduce security flaws based on these incorrect assumptions.

  6. AI-Driven Code Linter Exploits: An attacker modifies an AI-powered code linter so that it suggests suboptimal security practices. Developers, trusting the AI's recommendations, could unintentionally introduce vulnerabilities into their code.

  7. Stealthy Code Theft via AI Suggestions: An AI-powered development tool stealthily suggests code snippets that include a hidden functionality to send copies of the codebase to a remote server under the attacker’s control, effectively resulting in intellectual property theft.

Limitations of Static Code Analyzers

Static code analyzers are usually used to secure code, yet they're often constrained by their rule-based nature. They excel in identifying known patterns of vulnerabilities but struggle with novel or complex security issues that require contextual understanding.

This is the reason why static code analyzers such as Snyk are subpar as compared to an LLM-powered vulnerable software detector.

A Better Alternative: An LLM-powered Vulnerability Analyzer

LLM-powered vulnerability analyzer stands out by not only identifying a wide array of vulnerabilities, including those that may elude static analyzers, but also suggesting precise fixes. They analyze the context of the code, understand its intent, and propose improvements or security patches.

Enhanced Detection and Fixing:

  1. Contextual Awareness: LLMs can understand the context surrounding a piece of code, enabling them to identify subtle vulnerabilities that are not just pattern-based.

  2. Adaptive Solutions: They propose solutions that are not one-size-fits-all but tailored to the specific needs and contexts of the project at hand.

Here's a screenshot from a paper, "Can Large Language Models Find and Fix Vulnerable Software?" by David Noever:

Working Solution

We built an initial version of the working solution to help you protect your software supply chain via an LLM-powered vulnerability analyzer. Here's a demo for the same:

Collaboration Between Human Expertise and LLMs

Despite the impressive capabilities of LLMs in enhancing code security, the discerning judgment of human developers remains crucial. Developers can interpret LLM suggestions with an understanding of the broader application context, making informed decisions about which recommendations to implement. This collaborative approach leverages the best of both worlds: the efficiency and insight of LLMs, and the nuanced understanding and experience of human developers.

The integration of LLM-powered tools into the software development lifecycle marks a significant leap forward in building secure, robust applications. By effectively balancing the speed and convenience of AI-generated code with the security insights provided by LLMs—and tempered by human oversight—developers can navigate the challenges of modern software development more safely and efficiently.

Application Security for Generative AI


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