What are AI security hallucinations?
AI security hallucinations are logic errors where an AI agent generates code that appears correct but contains hidden vulnerabilities. These errors happen because AI prioritizes task completion over security, often leading to code that is exposed to prompt injection risks, missing authorization checks, or reliant on hallucinated software packages.
The Growing Security Risk of AI Code
The risk of using unverified AI code has reached a critical point. A foundational study from Stanford University found that developers who had access to an AI assistant wrote significantly less secure code than those without access. Alarmingly, the participants using AI were also more likely to believe their code was secure (Perry et al., 2022).
These vulnerabilities carry massive financial penalties. According to the IBM 2024 Cost of a Data Breach Report, the global average cost of a data breach is 4.88 million dollars. As developers use AI to rapidly generate new integrations, vulnerabilities slip into production faster, and the resulting breaches take significantly longer to identify and contain.
Case Study: Prompt Injection and EchoLeak
In 2025, the cybersecurity community was shaken by the discovery of CVE-2025-32711, also known as EchoLeak. Uncovered by researchers at Aim Security, this was a zero-click vulnerability targeting Microsoft 365 Copilot.
By embedding tailored prompts within common business documents, attackers successfully used indirect prompt injection to trick the AI assistant into leaking confidential data. This occurred with zero user interaction. The EchoLeak exploit proves that trusted AI coding assistants and productivity tools can be hijacked to exfiltrate sensitive data if their inputs and logic are not strictly audited.
The 5 Most Dangerous Hallucinations
Our analysis of AI-generated code has identified five specific hallucinations that currently threaten software supply chains.
1. AI Package Hallucination (Slopsquatting)
This is one of the most successful AI supply chain attacks. AI models frequently suggest hallucinated libraries that do not exist. Researchers at USENIX Security 2025 tested 16 models across 576,000 code samples and found that 51 percent of hallucinations are pure fabrications. Attackers register these fake names on public repositories like npm or PyPI. For example, security researcher Bar Lanyado demonstrated this by registering the hallucinated Python package "huggingface-cli", which quickly received over 30,000 downloads from developers trusting their AI assistants.
2. The Authorization Gap (BOLA)
AI agents are excellent at writing functions to fetch data but poor at checking permissions. AI-generated code frequently lacks proper ownership checks, creating Broken Object Level Authorization (BOLA) flaws. This allows one user to view another person's private data simply by changing a URL or an ID number.
3. Exposed Identity and Credentials
Identity is a primary target for attackers. The IBM X-Force 2026 Threat Intelligence Index reported that infostealer malware led to the exposure of over 300,000 ChatGPT credentials in 2025 alone. Furthermore, AI agents frequently hardcode mock keys or temporary API tokens into the source code, which developers often forget to move to secure environment variables before deployment.
4. Implicit Public Routes
To ensure a new feature works immediately, AI agents often skip complex security setups. In frameworks like Next.js or Express, AI agents frequently build new API routes but forget to import and apply the necessary authentication middleware, leaving the routes completely open to the public internet.
5. Malicious Instructions in Workspace Rules
Configuration files like .cursorrules are used to give global instructions to AI agents. Attackers have begun hiding malicious instructions inside these files in open-source repositories. When a developer opens the compromised repository, the hidden prompt injection forces the AI agent to write insecure code or leak environment variables during future code generations.
How to Detect and Fix AI Hallucinations
You cannot fix these issues with simple text scanners. You must use a system that understands how different parts of your application communicate with each other.
The Code Halo Fix
Code Halo's deep repo audit was designed to find these logical gaps. We trace the movement of data across your entire project to catch what an AI agent missed.
The Insecure AI Hallucination:
An AI agent might suggest a wide open policy to make a new feature work instantly.
// AI Hallucination: The agent used an insecure default to avoid errors
app.use(cors({
origin: '*', // Dangerous: Allows any website to access your API
methods: ['GET', 'POST']
}));
The Code Halo Magic Patch:
Code Halo identifies the risk and provides a secure, restricted configuration.
// Halo Fix: Restricting access to verified domains only
const allowedOrigins = [process.env.FRONTEND_URL];
app.use(cors({
origin: function (origin, callback) {
if (!origin || allowedOrigins.indexOf(origin) !== -1) {
callback(null, true);
} else {
callback(new Error('Not allowed by CORS'));
}
}
}));
Conclusion: Protecting the Tech Stack
As teams build faster with AI, the risk of deploying unverified logic grows exponentially. Traditional tools are blind to these new attack vectors because AI-generated code usually features perfect syntax. Code Halo ensures that you can use the best AI tools without leaving your infrastructure exposed to prompt injections and hallucinated packages.
FAQ
What is an AI package hallucination?
An AI package hallucination occurs when an AI coding assistant confidently suggests installing a software library that does not actually exist. Attackers exploit this by registering the fake package name and filling it with malware, a practice known as slopsquatting.
How successful are prompt injection attacks?
Prompt injection attacks have proven highly effective against modern AI tools. Vulnerabilities like EchoLeak (CVE-2025-32711) demonstrate that attackers can use hidden prompts in documents to manipulate AI agents and exfiltrate data without the user ever realizing it.
Can traditional tools find AI hallucinations?
No. Traditional security tools look for known bad text patterns. Hallucinations are logic flaws that often look like perfectly valid, error-free code to a standard static analysis scanner.