Defending LLM Agents Against Parameter Poisoning

# Defending LLM Agents Against Parameter Poisoning

https://gcdradar.vercel.app/incidents/defending-llm-agents-against-parameter-poisoning · AIR-2026-1761 · live

- Lane: Research
- Severity: 1 (Research / proof of concept): Demonstrated research study with no known real-world exploitation.
- Fix status: Fix status unknown
- First reported: 2026-11-04
- AI role: Attack on AI
- Products: LLM agents

## Summary

Researchers analyze parameter poisoning vulnerabilities where indirect prompt injection alters tool arguments while preserving intended actions. The attack manipulates task-critical values without introducing new tool calls.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2026-11-04: Right Tool, Wrong Parameters: Defending LLM Agents Against Parameter Poisoning (https://arxiv.org/abs/2611.04673)

## Sources

- 2026-11-04 arXiv: Right Tool, Wrong Parameters: Defending LLM Agents Against Parameter Poisoning https://arxiv.org/abs/2611.04673

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.