Weekly Dev Log 2026-W08

Data-Based | Training Data Extraction | Training dataset (confidentiality) | Crafted prompts designed to trigger memorised content | Verbatim or near verbatim training data (text, PII, secrets) | Data-Based | Membership Inference | Training dataset membership (privacy metadata) | Known candidate data sample already possessed by the attacker | Yes/no (or probability) decision indicating whether the sample was used in training | Data-Based | Prompt Leakage / System Prompt Exposure (LLM07:2025) | System prompt / developer instructions | Prompts asking the model to reveal or reflect on its instructions | Partial or full disclosure of hidden system or developer prompts | Model-Based | Weight Extraction (Model Stealing) | Model parameters (intellectual property) | Large volumes of carefully chosen API queries | A surrogate or distilled model replicating the original model's behaviour | Model-Based | Model Inversion | Model's internal representations | Unknown or partially known data, or model embeddings/outputs | New training data or attributes reconstructed from the model | System-Based | Context Window Poisoning (Prompt Injection) | LLM context window (instruction hierarchy) | Attacker controlled text embedded in input or retrieved content | Altered behaviour, policy bypass, unintended actions | System-Based | Context Overflow / Unbounded Consumption (LLM10:2025) | Context window size and system resources | Excessively large prompts or documents | Truncated safeguards, degraded responses, or denial of service | System-Based | Stateful Conversation Manipulation (Memory Poisoning) | Persistent conversation memory | Malicious statements intended to be stored as long term context | Persistent misinformation or corrupted future responses | User-Based | LLM-Powered Social Engineering | Human cognition and decision-making | Contextual or personal information used to craft persuasive output | Manipulated users (phishing success, fraud, coerced actions) | User-Based | Trust Exploitation / Misinformation (LLM09:2025) | User trust and judgment | Confident but incorrect or maliciously framed prompts | Users accepting false, unsafe, or harmful information |