In this article, we break down the 10 leaders in AI SAST tools. We explore the core features of each tool and the unique ways they implement AI to enhance security discovery, prioritization and remediation.
What is SAST?
Static Application Security Testing (SAST) is a methodology for analyzing an application's source code, bytecode, or binary to identify vulnerabilities and security flaws early in the software development lifecycle (SDLC). SAST finds vulnerabilities within the source code, which means that it is often the first defense against insecure code.
For more information SAST vs DAST what you need to know
What vulnerabilities does SAST find in your code?
There are many different vulnerabilities SAST can find and it depends on the coding practices used, technology stack and frameworks. Below are some of the most common vulnerabilities a SAST tool will typically uncover.
SQL Injection
Detects improper sanitization of user inputs that could lead to database compromise.
Example Injection Code
# Function to authenticate user
def authenticate_user(username, password):
query = f"SELECT * FROM users WHERE username = '{user}' AND password = '{password}'"
print(f"Executing query: {query}") # For debugging purposes
cursor.execute(query)
return cursor.fetchone()
Cross-Site Scripting (XSS)
Identifies instances where user inputs are incorrectly validated or encoded, allowing injection of malicious scripts.
Example of code vulnerable to XSS
<script>
const params = new URLSearchParams(window.location.search);
const name = params.get('name');
if (name) {
// Directly inserting user input into HTML without sanitization
document.getElementById('greeting').innerHTML = `Hello, ${name}!`;
}
</script>
Buffer Overflows
Highlights areas where improper handling of memory allocation could lead to data corruption or system crashes.
Example code vulnerable to buffer overflow
#include
void vulnerableFunction() {
char buffer[10]; // A small buffer with space for 10 characters
printf("Enter some text: ");
gets(buffer); // Dangerous function: does not check input size
printf("You entered: %s\n", buffer);
}
int main() {
vulnerableFunction();
return 0;
}
Insecure Cryptographic Practices
Finds weak encryption algorithms, improper key management, or hardcoded keys.
import hashlib
def store_password(password):
# Weak hashing algorithm (MD5 is broken and unsuitable for passwords)
hashed_password = hashlib.md5(password.encode()).hexdigest()
print(f"Storing hashed password: {hashed_password}")
return hashed_password
SAST tools provide valuable insights, enabling developers to fix issues before they become critical.
How AI is Enhancing SAST Tools
Right now you can’t get away from AI buzz (and BullSh*t). It can be difficult to know exactly how AI is being implemented in security tools. We wanted to compare some of the leaders in AI-powered SAST and explain the different ways these tools are implementing AI to enhance security.
Right now there are three trends with AI as it relates to SAST tools.
1. AI to improve vulnerability detection
- AI models trained on large datasets of known vulnerabilities improve the accuracy of identifying security issues while reducing false positives.
2. AI to create automated prioritization
- AI helps rank vulnerabilities based on severity, exploitability, and potential business impact, allowing developers to focus on critical issues first.
3. AI to provide automated remediation
- AI provides context-aware code fixes or suggestions, speeding up the remediation process and helping developers learn secure coding practices.
Top 10 AI-Powered SAST Tools
Here are 10 industry leaders that are using AI in different ways to enhance the capabilities of traditional SAST
(In alphabetical order)
Aikido Security | AI AutoFix
Core AI Capability | Auto Remediation (Dashboard + IDE)
Aikido Security uses AI to create code fixes for vulnerabilities discovered by its SAST scanner and can even generate automated pull requests to speed up the remediation process.
Unlike other tools, Aikido does not send your code to a third-party AI model and has a unique method of ensuring your code does not leak through AI models. Aikido creates a sandbox environment of your code, then a purpose-tuned LLM will scan it, and create suggestions which are also scanned again for vulnerabilities. Once the suggested remediation as passed validation, a pull request can be automatically created before finally the Sandbox environment is destroyed. Aikidos AutoFix is also able to give a confidence score on the suggestions it makes to developers to make informed decisions when using AI-generated code.
Checkmarx
Core AI Capability | Auto Remediation (IDE only)
Chckmarx SAST tools can provide AI-generated coding suggestions to developers within their IDE. The tool connects to ChatGPT transmits the developer's code to the OpenAI model and retrieves the suggestions. This method makes the process of querying ChatGPT easier but does not add any proprietary processes giving it limited capabilities right now.
WARNING - This use-case sends your proprietary code to OpenAI and may not meet compliance standards.
CodeAnt AI
Core AI Capability | Improved Detection (Dashboard)
CodeAnt is a code security and quality tool that entirely uses AI for its discovery of code vulnerabilities and suggested fixes. CodeAnt does not provide documentation on how their AI models work but generally uses AI as their core detection engine, this can slow down detection, particularly in large enterprises.
CodeThreat
Core AI Capability | Automated Prioritization (Dashboard)
CodeThreat offers on-premise static code analysis and provides AI-assisted remediation strategies. A core difference is that CodeThreat allows you to integrate your own on-premise AI model into their tool. This has the advantage of not sending data to a third party but means it can only offer genetically trained AI models right now and you need to be running an on-premise AI LLM like ChatGPT.
Fortify Static Code Analyzer
Core AI Capability | Improved Prioritization (Dashboard)
Fortify Static Code Analyzer scans source code for vulnerabilities and gives users the option to adjust thresholds when an alert is made for example likeliness of exploitability. Fortifies AI Autoassistant reviews the previous thresholds assigned to vulnerabilities and makes intelligent predictions on what the thresholds for other vulnerabilities should be.
Note: Fortify Static Code Analyzer does not use AI to discover vulnerabilities or suggest fixes for them, instead it uses it to predict administrative settings used in the admin panels.
GitHub Advanced Security | CodeQL
Core AI Capability | Auto Remediation (IDE + Dashboard)
GitHub CodeQL is a static code scanner that uses AI to create intelligent auto-remediation in the form of code suggestions. Developers can accept or dismiss the changes via pull requests in GitHub CodeSpaces or from their machine.
Qwiet AI | SAST Code
Core AI Capability | Auto Remediation (Dashboard)
Qwiet AI SAST is a rule-based static application security testing tool that leverages AI to auto-suggest remediation advice and code fixes for code vulnerabilities. Its core offering is its three-stage AI agents which Analyze the issue, Suggest a fix, and then validate the fix.
Snyk Code | DeepCode
Core AI Capability | Auto Remediation (IDE)
Snyk Code, a developer-focused, real-time SAST tool that can provide code suggestions to developers from within the IDE thanks toDeepCode AI which Snyk acquired. DeepCode AI utilizes multiple AI models and the core selling point is that their models are trained on data curated by top security specialists giving improved confidence in the AI results.
SemGrep SAST
Core AI Capability | Improved Detection
SemGreps AI assistant, aptly named assistant, uses the context of the code surrounding a potential vulnerability to provide more accurate results and provide recommended code fixes. It can also be used to create rules for SemGrep to enhance its detection based on the prompts you provide.
Veracode Fix
Core AI Capability | Auto Remediation
Veracode fix uses AI to suggest changes based on vulnerabilities within code when developers are using the Veracode IDE extension or CLI tool. The main differentiator for Veracode Fix is that their custom-trained model is not trained on code in the wild but on known vulnerabilities within their database. The positive of this will be more confidence in suggested fixes, the negative is that it is more limited in the scenarios that it can suggest code fixes.
How to choose a SAST tool
AI) is a relatively new entrant to the security market, and industry leaders are continuously exploring innovative applications. AI should be viewed as a tool to enhance security systems, rather than as a sole source of truth. It's important to note that AI cannot transform subpar tools into effective ones. To maximize its potential, AI should be integrated with tools that already have a robust foundation and a proven track record.
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