Privacy Attack

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

System design where humans validate or guide model outputs, especially for high-stakes decisions.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate ...

Maliciously inserting or altering training data to implant backdoors or degrade performance.

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Reconstructing a model or its capabilities via API queries or leaked artifacts.