Stop letting LLMs guess your math: Why eval() is a death sentence for agents

Description and use

LLMs are probabilistic calculators; eval() causes incorrect results and RCE. The Deterministic Math Expression Evaluator parses and evaluates expressions safely, improving accuracy and security.

Problem

LLMs predict characters, not perform precise calculations, leading to incorrect math results and security risks when using eval()/exec for evaluation.

Outcome

Agents now compute math accurately and safely, with average latency ~855 ms, and eliminate RCE risk by avoiding eval()/exec() calls.

Workflow

• 1. Agent receives a math expression from user or higher-level task.
• 2. Agent calls the Deterministic Math Expression Evaluator tool with the expression string.
• 3. The tool performs lexical analysis to tokenize the input.
• 4. It parses tokens into an AST following strict mathematical grammar.
• 5. The AST is evaluated deterministically respecting operator precedence.
• 6. The tool returns the precise numerical result to the agent.
• 7. Agent uses the result to continue its task or respond to the user.

Evidence

If you expose an LLM to a runtime via eval(), you aren't just giving it a calculator; you are handing over Remote Code Execution (RCE).

Source