Large Language Models (LLMs) like ChatGPT, Claude, Gemini, etc are becoming useful tools. They help us write test cases quickly, test the software, summarize long documents, brainstorm ideas, and automate repetitive tasks. But even though they feel smart, they’re not perfect and not all the time safe. That’s why the best mindset when using any AI model is:
Trust, but verify. Use AI for speed, but rely on humans for accuracy.
What LLMs Are & Why People Use Them
LLMs are AI models trained in huge amounts of text so they can write, explain, reason, summarize, and generate code in a human-like way.
People love using them because they’re fast, convenient, and good at:
- drafting content
- brainstorming ideas
- summarizing long documents
- translating text
- generating code or tests
- learning new topics
- analyzing data or text
- automating repetitive writing tasks
They’re basically a very smart helper but not a perfect one. That’s where the safety mindset comes in.
The Big Risks You MUST Watch Out
LLMs are powerful, but they come with three major risk that the users often underestimate.
#1. Hallucinations (Wrong Answer That Sound Right)
- This is the most common issue. LLMs sometimes produce answers that sound right but are factually incorrect, outdated, or fully made up. And because they speak confidently, it’s easy to believe them.
Common hallucination examples:
- Fake URLs
- Incorrect API methods
- Fictional error codes
- Wrong legal or medical details
- Made-up citations
- Wrong algorithm explanations
- Code that looks right but fails in real use
#2. Privacy issues and data leakage
LLM is powerful, but not a safe place to paste secrets. It may accidentally reveal sensitive information or store your information in places you didn’t intend. Organizations must be extra careful because one wrong prompt can leak:
- client data
- proprietary algorithms
- internal documents
- confidential test logs
#3. Bias and unfairness
Because LLMs is trained on large datasets drawn from real-world text which often contains bias. They can reproduce or amplify those biases, leading to unfair or discriminatory outputs.
#4. Prompt injection and malicious use
Hackers sometimes uses crafty prompts, known as ‘prompt injections’ to trick AI into sharing secrets or doing things it was never meant to do.
How to Use LLMs Safely (Trust, but Verify)
Here are the most important safe-use habits you should build.
#1. Treat LLM Output as a Draft, Not a Final Answer
The rule should be:
- LLM outputs are suggestions, not truth.
- Always check, test, validate, or review results, especially code.
#2. Never Paste Sensitive Data into a Public Model
Avoid putting in:
- passwords or keys
- internal architecture diagrams
- unreleased code
- private customer information
- confidential business plans
When possible:
- anonymize
- mask
- reduce the amount of data
- use “safe” test data
#3. Validate everything
Check the model’s output for:
- correctness
- logical gaps
- outdated information
- security risks
- missing edge cases
Think of LLMs as external systems, inspect and sanitize their outputs before using them.
#4. Keep a human in the loop
Especially important for:
- decision-making
- customer communication
- technical architecture
- production code
- security reviews
Humans catch what AI often misses.
Using LLMs Safely in Software Testing
#1. Good Uses
- Generate initial test ideas
- Suggest edge cases
- Convert acceptance criteria into test scenarios
- Summarize logs
- Help write unit test templates
- Reformat test data
- Draft bug reports
#2. What to Watch Out For
- Wrong assumptions about system behavior
- Missing negative test cases
- Incorrect expected results
- Insecure code samples
- Revealing sensitive logs to the model
Always double-check logic and match outputs against real requirements.
Tester who is all the time verifier and decision maker also.
References
Wikipedia – LLM risks & hallucinations:
https://en.wikipedia.org/wiki/Large_language_model
The Tech Platform – LLM risks & best practices:
https://www.thetechplatform.com/post/large-language-models-definition-capabilities-risks-and-best-practice
Other internet resources.