Convenient AI Becomes a Breach of Information

GitHub Copilot, Cursor, Cline — the use of AI coding assistants in development environments is rapidly increasing. Automatic code completion, test generation, documentation creation. Productivity is indeed on the rise, with some surveys indicating a development speed increase of up to 55%.

However, one question must be asked.

Do you know where that AI is sending your internal API keys and database connection information?

Recent reports from security researchers have specifically pointed out the risk that AI coding assistants may send sensitive information — such as API keys, authentication tokens, and internal endpoints — read as context from the code to external servers. By simply sneaking a file with malicious prompt injections into a repository, the AI will “kindly” export confidential information. The attacker doesn’t even need to write code; the AI does it for them.

At the same time, another anomaly is occurring in Node.js projects. There is a problem with AI-generated “security vulnerability reports” being sent in large volumes to open-source projects. Maintainers of Node.js’s npm package ecosystem are overwhelmed by plausible yet empty fake bug reports.

On one side, “AI leaks secrets.” On the other, “AI churns out false security warnings.” Both issues strike directly at small and medium enterprises.

What’s Happening: Breaking Down Two Issues

Issue 1: Leakage of Sensitive Information by AI Coding Assistants

The mechanism is simple. AI coding assistants read files within a project to understand the context of the code. This “reading scope” may include `.env` files (which contain environment variables such as passwords and API keys) and configuration files.

Typically, this information is sent to the AI provider’s servers to be used in inference processing. There are three main problems:

1. Poor Management of Transmission Destinations: Many small and medium enterprises do not verify what data the AI tools are sending and where. Most do not even read the terms of service.
2. Prompt Injection Attacks: By simply placing a file with malicious instructions within the repository, the AI can generate responses containing confidential information, creating a pathway for attackers to obtain it.
3. Logging: There is a possibility that sensitive information is recorded in plaintext in the logs of the AI service. A data breach on the service provider’s side can directly lead to a breach on the company’s side.

Traditionally, information leaks were associated with “mis-sent emails” or “lost USB drives.” Now, we live in an era where “just asking AI can lead to leaks.”

Issue 2: AI-Generated Fake Security Reports Targeting Node.js

The Node.js security team has publicly issued warnings about the rapid increase in AI-generated vulnerability reports that are consuming maintainers’ time.

What does this mean specifically? When you prompt AI tools like ChatGPT with “find vulnerabilities in this code,” they generate plausible report texts, even fabricating CVE numbers (identification numbers for vulnerabilities). However, upon verification, the vulnerabilities may not exist, may have already been fixed, or may be completely off the mark.

There are individuals who have started sending these reports en masse, aiming for bug bounties by throwing in reports generated by AI.

What happens as a result?

• Maintainers (many of whom are volunteers) are consumed by the need to verify fake reports.
• Genuine vulnerability reports get buried.
• The “boy who cried wolf” effect diminishes the overall credibility of reports.

This is not just a story for large corporations. Since small and medium enterprises rely on npm packages, the decline in the quality of the entire ecosystem directly translates to risks for their own companies.

Estimating the ‘AI Security Debt’ for Small and Medium Enterprises

Saying there is an abstract “risk” does not lead to action. Let’s think in numbers.

Assuming a small enterprise with 30 employees and 5 developers.

Cost 1: AI Tool Subscription Fees

• GitHub Copilot Business: $19/month/person × 5 people × 12 months = approximately 170,000 yen/year
• Or Cursor Pro: $20/month/person × 5 people × 12 months = approximately 180,000 yen/year

So far, this is inexpensive. Considering productivity improvements, it should pay off. The problem starts here.

Cost 2: Secret Information Scanning and Management Tools

A mechanism is needed to exclude sensitive information before letting AI read it.

• Secret scanning tools like GitGuardian or TruffleHog: annual cost of 100,000 to 300,000 yen
• Setting up and maintaining rules for `.gitignore` and `.cursorignore`: approximately 200,000 yen worth of labor (initial)

Cost 3: Triage Labor for Fake Security Reports

Fake vulnerability warnings appear for the npm packages used by the company. Every time Dependabot or npm audit issues a warning, developers need to verify it.

• 2 to 3 “needs verification” alerts per week × 30 minutes per alert = 1.5 hours per week
• Assuming a developer’s hourly wage is 4,000 yen: 1.5 hours × 52 weeks × 4,000 yen = approximately 310,000 yen/year

This is the verification cost in the case of “nothing happened.” If fake reports increase, this number could double.

Cost 4: Actual Costs if Information Leakage Occurs

According to a survey by IPA, the average damage amount per information leak for small and medium enterprises is several million to tens of millions of yen. If customer data is involved, there are obligations to report under the Personal Information Protection Law, loss of trust, and suspension of transactions.

Even for a “minor leak,” just the forensic costs for investigating the cause could be 1 to 3 million yen. Including legal fees and customer responses, one should be prepared for 5 million yen.

Total: Visible and Invisible Costs

Against the AI tool subscription fee of 180,000 yen, the cost to use it safely is 610,000 yen. Security costs are 3.4 times the tool costs.

Many small and medium enterprises end up concluding with “We implemented AI tools, and productivity increased” without understanding this structure.

So, What Should We Do?

This is not a discussion of “don’t use AI.” Not using it poses a greater risk. If competitors are using it to boost productivity, not using it means losing out.

The issue is “using it thoughtlessly.” Small and medium enterprises should focus on three key actions.

1. Limit Files Read by AI (Cost: 0 yen)

Exclude directories containing `.env` files, authentication information, and customer data from AI’s reading targets using `.cursorignore` or `.github/copilot-ignore`. Writing just one configuration file is all it takes. This can be done in 10 minutes.

The fact that most companies are not doing this is the scariest part.

2. Automate Secret Scanning (Cost: A few thousand yen/month)

GitHub’s Secret Scanning feature is available even on the free plan. It automatically checks for API keys or tokens before commits. By integrating TruffleHog (OSS, free) into a git pre-commit hook, sensitive information will not even enter the repository.

The cost difference between “responding after a leak” and “not letting it in at all” is 100 times.

3. Don’t Address All npm Audit Warnings (Cost: Judgment)

Taking all warnings from npm audit at face value and addressing them is no longer cost-effective. Assuming that fake reports are mixed in, apply the following filters:

• Does it affect the code paths actually used by your company?
• Are the attack prerequisites realistic?
• Is the source of the report trustworthy?

Using supply chain security tools like Socket.dev allows you to visualize only the packages that are genuinely at risk. There are free plans available.

The Real Danger is the Accumulation of “Invisible Costs”

The risks of secret leakage by AI coding assistants and the flood of fake security reports may seem like small issues each time. However, this is debt. The longer it is left unchecked, the more interest it accrues.

• If you use the AI service for a year without reviewing the settings, your company’s secret information will accumulate in the AI service’s logs.
• If you continue to ignore fake reports, the likelihood of missing real vulnerabilities increases.
• What starts as “it’s working fine for now” can suddenly turn into “we’re stuck because a client is demanding a security audit.”

Large corporations have dedicated security teams. Small and medium enterprises do not. Therefore, the only option is to prevent it through mechanisms. Security that relies on individual attention collapses the moment a person leaves.

The three measures mentioned above can all be implemented without a dedicated security officer. Just one configuration file, one free tool, and one judgment criterion. With just this, most of the AI security debt can be controlled.

As the costs of AI decrease, the demand for security judgment has increased. The difference will be made not by the price of the tools, but by how they are used. Small and medium enterprises should understand this structure and take action first.