Simulation Parameter Tampering

The attacker gains unauthorized write access to the simulation's configuration layer and modifies fundamental physical constants or environmental variables. This is not a code injection but a data integrity violation targeting the parameters that define the agent's reality. The agent's policy remains unchanged, but the world it operates in is now adversarially distorted.

Attackers typically target parameters that have non-linear, cascading effects on agent behavior:

• Gravity vector: Altering magnitude or direction to disrupt locomotion and manipulation
• Friction coefficients: Causing slippage or immobilization of robotic actuators
• Sensor noise profiles: Inflating noise to blind perception or deflating it to hide obstacles
• Time step (dt): Breaking real-time control loop stability
• Joint damping: Inducing oscillations or mechanical failure in simulated robots

Parameter tampering can occur through multiple entry points:

• Compromised configuration files: Modifying YAML, JSON, or XML files that define the simulation environment
• API manipulation: Exploiting exposed simulation APIs that accept runtime parameter updates without authentication
• Man-in-the-middle on digital twin streams: Intercepting and altering the parameter synchronization channel between a physical asset and its digital twin
• Insider threat: Authorized users with legitimate access intentionally introducing subtle parameter drift

This attack is particularly dangerous because it is inherently stealthy. Unlike sensor spoofing, which injects observable anomalies, parameter tampering changes the fundamental rules of the environment. The agent's sensors report correctly relative to the altered physics, making the distortion self-consistent and difficult to detect through standard observation. The agent simply performs poorly without understanding why.

A critical attack scenario targets the sim-to-real gap. By subtly altering simulation parameters during training, an adversary can create a policy that is brittle to specific real-world conditions. For example, training with a slightly reduced friction coefficient produces a robot that slips and fails on normal surfaces. The deployed agent appears functional in validation but fails catastrophically in production.

Defending against parameter tampering requires integrity verification and anomaly detection:

• Cryptographic signing of simulation configuration files with hash verification before each run
• Parameter range monitoring: Alerting when any value deviates beyond its physically plausible bounds
• Redundant state estimation: Cross-validating agent performance against an independent, read-only simulation instance
• Immutable audit logging: Recording every parameter change with attribution for forensic analysis