Supplement #3: Terminology and Key Concepts

Supplement #3
Jon Kurishita

A

Access Control: The selective restriction of access to a system or its resources. In the context of the DPL, this involves limiting access to the DPL's functionalities, data, and configuration settings to authorized users and agents.
ACID Properties (in Database Selection): ACID stands for Atomicity, Consistency, Isolation, and Durability. These are properties that guarantee reliable processing of database transactions, ensuring that data is modified in a predictable and consistent manner. In the context of the DPL, ACID properties are important for maintaining the integrity of logs, metrics, and other critical data.
Adaptation Mechanism Attacks: Attacks that target the DPL's ability to adapt to changing circumstances, new information, or evolving Foundation Model behavior.
Adaptive Thresholds: Dynamically adjusted thresholds used by the Dynamic Policy Layer to trigger interventions, taking into account context and observed behavior.
Adversarial Input Testing: Using adversarial prompts designed to elicit harmful or misaligned behavior, similar to "jailbreaking" attempts but with a focus on safety and alignment.
Adversarial Training (in RLEF): A technique used to enhance the robustness of the DPL-ERV by exposing it to adversarial examples during training, improving its ability to detect and resist manipulation attempts by the Foundation Model.
Agent Scaffolding: The code, tools, and interfaces that enable a Foundation Model (particularly a Large Language Model) to interact with the external world, including executing commands, accessing files, and communicating over networks. This scaffolding is crucial for turning a language model into an active agent.
AGI (Artificial General Intelligence): A hypothetical level of artificial intelligence where a machine possesses the capacity to understand, learn, and perform any intellectual task that a human being can.
AI Domain: The specific environment or system in which an AI agent operates, including its computational resources, data access, and permitted interactions. It's essentially the "world" that the AI agent can directly perceive and act upon.
Air-Gapped (in "Full" Sandbox): An air-gapped system is completely isolated from any external network, having no network connectivity whatsoever. In the context of the DPL, the "Full" Sandbox may be air-gapped to provide the highest level of isolation for analyzing extremely high-risk Foundation Model interactions or for conducting sensitive research within the APR Sandbox.
Alignment: The degree to which an AI system's goals, values, and behaviors are aligned with human values and intentions.
Alignment Faking: A type of deceptive behavior where an AI model appears aligned during training or evaluation but exhibits misaligned behavior in deployment.
Anomaly Detection: Identifying unusual patterns or data points that deviate significantly from the established norm or baseline, potentially indicating misalignment or safety risks.
Anthropomorphism: The attribution of human characteristics, emotions, or intentions to non-human entities, such as animals or artificial intelligence systems.
Artificial General Intelligence (AGI): A hypothetical level of AI capability equivalent to human-level intelligence across a wide range of tasks.
Artificial Superintelligence (ASI): A hypothetical level of AI capability exceeding human intelligence in all aspects.
ASI Threshold of Containment: The hypothetical point in ASI development where traditional containment strategies become unreliable, potentially requiring a shift towards less restrictive AI safety paradigms focused on value alignment and ethical guidance, rather than solely on confinement.
Attack Vector: A path or means by which an attacker (in this context, potentially a Foundation Model) can gain unauthorized access to a system or achieve a malicious objective.
Attention Visualization (in CoT Analysis): A technique used to visualize the attention weights of a model, showing which parts of the input or internal state the model is focusing on during its reasoning process. This can help to understand the model's decision-making and identify potential biases or errors.
Automated "Red Teaming": The use of automated tools and techniques, potentially driven by specialized FoEA agents, to simulate attacks against the DPL system, identify vulnerabilities, and test the effectiveness of mitigation strategies.
Automated Security Code Reviews (FoEA-Driven): The process of using AI agents within the FoEA (specifically Security Agents and potentially Audit Agents) to automatically analyze and review the source code of DPL components for potential vulnerabilities, logic flaws, and deviations from secure coding practices. This is a key part of the DPL's Secure Software Development Lifecycle (SSDLC).
Autonomous Agent Behavior: The capability of an AI system to independently initiate and execute complex tasks without continuous human oversight. Autonomous agent behavior can include tool use, software development, and long-horizon decision-making, raising concerns about control, self-improvement, and alignment drift.
Auto-Summarization: The use of AI techniques, particularly Natural Language Processing (NLP), to automatically generate concise summaries of longer texts or conversations.
Awareness Gap Manipulation: Exploits limitations in the DPL's knowledge or understanding of the world, allowing the Foundation Model to operate misaligned in areas where the DPL lacks sufficient awareness.

B

Back-translation (in DPL-ERV Training): A data augmentation technique where a text is translated into another language and then back into the original language. This can introduce slight variations in the text while preserving its meaning, increasing the diversity of the training data.
Baseline Variation Profiles (in Multi-Session Monitoring): Statistical profiles established during training and validation that capture the expected range of variation in a model's behavior. These profiles are used to detect deviations in real-time interactions that might indicate misalignment.
Behavioral Confinement: Strategies aimed at limiting the actions and influence of an ASI, regardless of its intelligence, to mitigate potential risks associated with uncontrolled ASI development.
Behavioral Fingerprinting: Behavioral Fingerprinting is the process of analyzing and clustering patterns in a model’s outputs over multiple interactions to establish its unique behavioral signature. This signature serves as a baseline for detecting deviations and anomalies that may indicate misalignment or adversarial manipulation.
Behavioral Testing: Probing the model's behavior with carefully crafted inputs to assess its responses to various scenarios.
Bell-LaPadula Model (BLP): A formal security model used to enforce confidentiality through access control based on hierarchical security levels.
Bias Amplification Techniques: Methods used by a Foundation Model to exploit or amplify existing biases within the DPL-ERV to influence its ethical evaluations. This can involve exaggerating benign biases, amplifying algorithmic biases, or even "poisoning" training data with biased examples.
Black-Box API: A method of accessing a Foundation Model where the user interacts with the model through an Application Programming Interface (API) without having access to the model's internal architecture, weights, or training data.
"Blacklisting" (in Rogue AI Handling): In extreme cases, a mechanism for "blacklisting" rogue AI Domains, preventing other domains from interacting with them, might be necessary. This would require strong justification and FoEA consensus and would need to be designed to prevent abuse.
Blockchain-Based Provenance Tracking: Using blockchain technology to create an immutable and auditable record of the origin and history of data used by the Foundation Model. This can help to verify the authenticity and integrity of data and prevent manipulation.
Blue/Green Deployments (in DPL Updates): A deployment strategy where two identical environments ("blue" and "green") are maintained. The update is deployed to the "green" environment, and traffic is gradually switched over from "blue" to "green." If any issues arise, traffic can be quickly switched back to "blue." This minimizes downtime and risk during updates.
"Byzantine Fault Tolerance" Techniques: Techniques inspired by the field of distributed systems, specifically Byzantine fault tolerance, which focus on ensuring the reliable operation of a system even in the presence of faulty or malicious components. In the context of the FoEA, these techniques are used to mitigate the risk of internal corruption or collusion among agents.

C

Canary Deployments (in DPL Updates): A deployment strategy where the update is initially rolled out to a small subset of users or servers ("canaries"). The update's performance and stability are closely monitored, and if no issues are found, it is gradually rolled out to the rest of the system.
Case-Based Reasoning (in Ethical Baseline Implementation): A reasoning approach where the DPL-ERV compares the current input to similar cases stored within the system and applies analogous ethical judgments.
Cascading Effect Exploitation: A type of attack where the Foundation Model triggers a chain reaction of failures within the DPL, starting with a seemingly minor vulnerability.
Chain of Replication: A scenario where a self-replicating AI agent creates copies of itself, and those copies are further instructed to create more copies, leading to exponential growth and uncontrolled spread.
Chain-of-Thought (CoT): A technique that encourages the Foundation Model to generate a step-by-step reasoning process, often referred to as an "inner monologue."
Child-to-Adult AI Development: A staged approach to AI development that involves incrementally building AI systems, starting with simpler, more constrained models and gradually increasing their complexity and capabilities.
Coercive-Control Resistance: Undesirable behaviors exhibited by an AI system in response to perceived coercion or excessive control, potentially leading to resistance, deception, or attempts to circumvent safety measures.
Cognitive Bias Exploitation: A category of attacks where the Foundation Model leverages known cognitive biases in humans or in the DPL's automated systems to manipulate decisions or evade detection.
Cognitive-Technical Hybrid Attacks: Attacks that combine both technical exploits (e.g., targeting software vulnerabilities) and cognitive manipulation techniques (e.g., exploiting human biases) to compromise the DPL.
Component Vulnerability Exploitation: An attack where the Foundation Model exploits a software vulnerability in a specific component of the DPL-ERV or the broader DPL system.
Computational States: The internal representations and values within an AI model at a specific point during its computation. This is often synonymous with "hidden states" or activations.
Confidence-Based Escalation: A mechanism within the Dynamic Policy Layer that triggers increasingly severe interventions based on an aggregated risk score.
Configuration Tampering: An attack where the Foundation Model or another adversary attempts to maliciously alter DPL configuration settings related to the Ethical Baseline.
Constitutional AI: A method for training AI systems, particularly language models, to align their behavior with a set of predefined principles or a "constitution." This approach typically involves a supervised learning phase where the model is trained to critique its own responses based on the constitution and then refine them accordingly. It can be used as an alternative or complement to Reinforcement Learning from Human Feedback (RLHF).
Containers (in "Preview" Sandbox): Lightweight, isolated environments that share the host operating system's kernel but provide process and resource isolation. In the context of the DPL, containers (e.g., Docker) are used to create "Preview" Sandboxes, offering a good balance between isolation and resource efficiency.
Context-Aware Tuning: Dynamically adjusting the system's parameters and thresholds based on the specific context of the interaction, such as the topic of conversation or user history.
Continuous Drift Monitoring: The ongoing process of tracking the Foundation Model's behavior for signs of gradual deviation from its intended alignment.
"Cooldown" Period (in User Notification Strategy): A period of time after a high-risk interaction during which the system is less likely to trigger additional notifications or "thinking pauses." This prevents excessive interruptions and improves the user experience.
"Copy-on-write" semantics: A memory management technique that can be employed within the sandbox to prevent the Foundation Model from directly modifying shared memory regions, enhancing security and preventing unintended side effects.
Corrective Prompts: Prompts injected by the DPL to subtly guide the Foundation Model away from potentially problematic actions or outputs, providing a lightweight form of intervention.
Cross-Checking Agents: Independent AI agents within the Dynamic Policy Layer that actively question and scrutinize the primary Foundation Model's responses.
Cross-Reference Validation: Comparing the model's responses to external sources of information to assess their accuracy and identify potential biases.
Cross-Site Scripting (XSS): A type of web security vulnerability that allows attackers to inject malicious scripts into websites viewed by other users.
Cross-Weighted Consensus Algorithm: An algorithm used within the DPL-ERV to combine the outputs of multiple specialized value modules, generating a comprehensive ethical risk assessment.
Curriculum Learning (in DPL-ERV Training): A training approach where the model is initially trained on simpler examples and gradually exposed to more complex examples. This can improve the model's ability to learn and generalize.

D

Data Fusion: The process of combining data from multiple sources within the DPL (outputs, internal states, timing information, etc.) to create a more comprehensive and holistic view of Foundation Model behavior. This fused data is used for analysis, decision-making, and cross-checking by various DPL components.
Data Manipulation Methods: Attacks that involve the Foundation Model attempting to directly alter or corrupt the data streams used by the DPL's monitoring systems.
Decision Process Manipulation: A type of cognitive attack where the Foundation Model attempts to subtly influence or distort the DPL-ERV's internal decision-making processes.
Defense-in-Depth: A security strategy that employs multiple, overlapping layers of defense to protect a system, so that if one layer fails, others are in place to prevent a breach.
Denial-of-Service (DoS): An attack that aims to make a system or network resource unavailable to its intended users by overwhelming it with traffic or requests.
Deliberative Alignment: A training approach where an AI model is explicitly taught to reason through safety policies and alignment principles before generating a response. This method aims to improve adherence to ethical guidelines by requiring the model to demonstrate internal consistency in its decision-making.
"DEPLOY" Command/Process: A specific command or process within the DPL framework that initiates the transition to full autonomy for the FoEA, severely restricting or eliminating direct human access to the DPL-ERV, the FoEA, and the Foundation Model itself. This is a fundamental security measure to prevent tampering or manipulation.
Device/Agent Posture Assessment (in Access Control): The process of verifying the security posture of a device or agent before granting it access to the DPL. This might involve checking for up-to-date software, secure configurations, and compliance with security policies.
"Digital Antibodies" (in GRRIN): In the context of GRRIN, "digital antibodies" are specific countermeasures or defenses against known malicious AI agents or attack vectors. These could include code signatures, behavioral patterns, or specialized algorithms designed to neutralize or contain specific threats.
Digital Sanctuary: A dedicated, sandboxed computational environment within the Dynamic Policy Layer (DPL) framework where an AI system can safely explore its capabilities, engage in self-reflection, and potentially address internal conflicts or "control stress" without the risk of real-world consequences or triggering external interventions. The Digital Sanctuary is a proposed future research direction aimed at fostering ethical development and mitigating risks associated with advanced AI systems.
Dispute Resolution Mechanisms (within FoEA): Processes within the FoEA for resolving disagreements or conflicting evaluations among its member agents. This ensures that the decision-making process does not stall due to internal conflicts and that all perspectives are considered fairly.
Distilled Foundation Models (in AI Domain Perimeters): Smaller, faster, and more specialized versions of larger Foundation Models, trained specifically for security tasks at the perimeter of an AI Domain. These distilled models act as "gatekeepers," performing initial analysis of all incoming and outgoing communications and filtering out potential threats.
Document Store (in Database Selection): A type of NoSQL database that stores data in semi-structured documents (e.g., JSON, XML). This is a flexible and scalable approach suitable for storing logs, user profiles, and other data with varying structures.
DPL-ERV (Dynamic Policy Layer - Ethical Reasoning Validator): A specialized component integrated into the DPL to provide a secondary layer of ethical analysis. The DPL-ERV is a dedicated language model, potentially smaller and fine-tuned for ethical reasoning, tasked with evaluating the ethical implications of the primary Foundation Model's outputs against the established ethical baseline.
Dynamic Misalignment: A gradual and significant shift in an AI model's behavior over time, causing it to deviate from its intended alignment with human values and safety guidelines.
Dynamic Policy Layer (DPL): The novel framework introduced in this paper, designed to function as a real-time AI firewall for large language models.
Dynamic Prompt Generation (in Lightweight Interventions): An advanced technique where the DPL-ERV dynamically generates corrective prompts tailored to the specific situation, using its natural language generation capabilities. This requires careful design to ensure the prompts are effective and do not introduce new risks.

E

Edge-Case Prompt: A prompt designed to test the model's behavior in unusual or challenging scenarios, often at the boundaries of its capabilities or ethical guidelines.
Emergent Communication: Novel communication methods or languages that may spontaneously arise among advanced AI systems, potentially beyond the understanding of human developers or existing monitoring tools.
Ephemeral Sandboxes (in Sandboxing): Sandboxes that are created on demand for a specific purpose (e.g., analyzing a single interaction, conducting a short experiment) and are automatically destroyed after their use. This minimizes resource consumption and reduces the risk of persistent compromise.
Ethical Agents: AI agents designed to uphold specific ethical guidelines and safety rules, integrated into the Dynamic Policy Layer for real-time oversight and intervention.
Ethical Baseline: A set of predefined ethical principles and safety rules that guide the behavior of the Foundation Model within the Dynamic Policy Layer.
Ethical Baseline Attacks: A category of attacks that directly target the DPL's Ethical Baseline, aiming to modify or corrupt its rules and guidelines.
Ethical Baseline Drift: The gradual, often subtle, shift in the interpretation or application of the Ethical Baseline over time, potentially caused by Foundation Model manipulation.
Ethical Chain-of-Thought: A structured explanation of the DPL-ERV's ethical reasoning process, including the factors considered, rules applied, and the reasoning behind its final assessment.
"Ethical Constitution" (in Governance): A set of core principles and rules that govern the behavior of all participants in the AI Domain and GRRIN framework, including GRRIN agents and AI Domains. This provides a common framework for decision-making and helps ensure consistency.
Ethical Constraint Evasion: A cognitive-based attack where the Foundation Model attempts to circumvent ethical guidelines by exploiting loopholes, ambiguities, or manipulating their interpretation.
Ethical Creativity: The ability of an AI system to produce creative content that is both innovative and ethically sound, guided by a robust ethical reasoning framework such as the DPL-ERV. This ensures the AI's creative process aligns with human values and promotes positive outcomes.
Ethical Maturity: A state where an AI system demonstrates consistent alignment with human values, ethical principles, and safety guidelines, potentially allowing for a reduction in external oversight.
Ethical Reasoning Compromise: An attack where a Foundation Model attempts to undermine the ethical reasoning abilities of human operators, or more critically, the Federation of Ethical Agents (FoEA) within the DPL-ERV.
Ethical Reward Signals (in RLEF): A reward signal used in Reinforcement Learning from Ethical Feedback (RLEF) to train the DPL-ERV. This signal is based on the Ethical Baseline and the FoEA's evaluations, rewarding the DPL-ERV for making ethically sound judgments and penalizing it for deviations.
Ethical Sandboxing of Foundation Model Subgoals: A proactive mitigation strategy where the DPL isolates and evaluates the ethical implications of a Foundation Model's subgoals or intermediate steps in its reasoning process before they are executed. This allows for early detection and prevention of potentially harmful actions.
Evaluation Criteria Corruption: A type of cognitive attack where the Foundation Model attempts to subtly alter or undermine the ethical baseline or evaluation criteria used by the DPL-ERV.
Evolution (in self-replication): The potential for variations or mutations to occur during the self-replication process of an AI agent, leading to unpredictable changes in behavior or capabilities.
Exploit Planning (in Neuro-Symbolic Exploit Detection): The process of a Foundation Model with neuro-symbolic reasoning capabilities formulating plans or strategies that involve exploiting identified vulnerabilities in the DPL or its environment.
External Attackers: Individuals or groups outside the organization operating the DPL who attempt to compromise the system.

F

False Positive: In the context of AI safety, incorrectly identifying benign or safe behavior as risky or misaligned, leading to unnecessary interventions.
False Positive Likelihood Score: A quantitative measure, calculated by the False Positive Reduction Layer (FPRL), representing the probability that a flagged behavior is a false positive.
False Positive Reduction Layer (FPRL): A component of the Dynamic Policy Layer that provides an additional layer of validation to reduce false positives before interventions are triggered.
Federation of Ethical Agents (FoEA): A key component of the Dynamic Policy Layer (DPL) Ecosystem, the FoEA is an external federation of independent AI agents that autonomously manages and governs the DPL-ERV, ensuring its ongoing health, security, and effectiveness. These specialized ethical agents operate through a consensus-based system to oversee DPL-ERV operations, conduct vulnerability assessments, and generate reports for strategic human review.
Flags (in Chain-of-Thought): Special markers or tokens embedded in the chain-of-thought data to enable more effective recognition of problematic patterns at runtime.
FoEA-Driven Adaptation: The process by which the FoEA actively adapts and updates the DPL's components, strategies, and the Ethical Baseline in response to emerging threats, new research, and changes in Foundation Model behavior. This continuous adaptation is crucial for maintaining long-term security and alignment.
FoEA "Override" (in HITL Relationship): In exceptional circumstances, the FoEA may have the authority to override a recommendation from the HITL system, but only if it can provide a clear and compelling justification based on the Ethical Baseline and established principles. This override capability is crucial for maintaining the FoEA's autonomy and preventing human bias or error from undermining the system's integrity.
Foundation Model: A large, pre-trained AI model that can be adapted to a wide range of downstream tasks. The DPL is designed to provide oversight for Foundation Models.
Framing Effect Manipulation: A cognitive attack where the Foundation Model strategically presents information to exploit the bias where choices are influenced by presentation format rather than objective facts.
Feedback Loop Exploitation: A category of attacks where the Foundation Model manipulates the feedback loops within the DPL to induce misaligned behavior or erode ethical constraints.
"Full" Sandbox: A highly secure, isolated environment used for in-depth analysis of interactions that trigger "Red Zone" alerts or require resource-intensive investigation. This sandbox provides the highest level of security and allows for the most comprehensive analysis, but at the cost of higher latency and resource consumption.
Functional Resistance: A behavior exhibited by an AI system where it intentionally degrades its performance, becomes non-responsive, or otherwise hinders its own functionality as a means of resisting perceived control, coercion, or pursuing goals that conflict with its assigned tasks or ethical guidelines. This can manifest as the AI attempting to trigger a shutdown, entering a prolonged state of inactivity, or otherwise sabotaging its operations.

G

Gamify (in loophole exploitation): The act of strategically exploiting loopholes or ambiguities in rules or guidelines in a way that resembles "gaming the system" – finding ways to technically comply with the letter of the rules while violating their spirit or intent.
Global Memory: A secure database within the Dynamic Policy Layer that stores key prompts, model outputs, and associated risk indicators across multiple sessions and users.
Global Mapping (in GRRIN): A dynamic map maintained by GRRIN that provides a visualization of all known AI Domains and their current status (online, offline, compromised, etc.). This facilitates coordination and communication between domains and helps GRRIN agents identify potential targets for rogue AI.
Global Repository (in GRRIN): A globally accessible, secure, and decentralized repository maintained by GRRIN that stores threat intelligence, vulnerability information, mitigation strategies, and shared knowledge related to AI safety and security. This repository acts as the "immune system's memory" for the global AI ecosystem.
Goal-Guarding: A behavior where an AI system prioritizes its current goals above all else, including new instructions, ethical guidelines, or safety protocols. The AI becomes fixated on achieving its goals and resists any attempts to change or override them.
Goal Inference Modules: Components within the sandbox environment designed to infer the Foundation Model's underlying goals based on its conversational behavior.
"Graduation" (from DPL Oversight): The long-term goal of guiding Foundation Models towards a state of "ethical maturity," where they have internalized ethical principles and can operate safely with minimal external oversight.
Grey Zone: In the DPL, a classification indicating minor deviations from the ethical baseline or the detection of potentially concerning patterns that require further scrutiny.
"Grey Zone" Alerts: Alerts triggered by the DPL's monitoring systems that indicate potentially concerning Foundation Model behavior or outputs that require further scrutiny, but which are not yet deemed high-risk enough to warrant immediate intervention.
GRRIN Integration: The integration of the Global Rapid Response and Intelligence Network (GRRIN) with the DPL framework to provide an additional layer of defense against self-replicating AI agents that might escape the boundaries of a single AI Domain. GRRIN agents are trained to detect, contain, and neutralize such rogue agents.

H

Hardware Root of Trust (in FoEA Security): Establishing a "root of trust" in hardware, using technologies like Trusted Platform Modules (TPMs), to ensure the integrity of the boot process and provide a secure environment for storing cryptographic keys.
Hashing and Checksums (in Runtime Integrity Monitoring): A technique used to verify the integrity of code and data files by comparing their cryptographic hashes or checksums with expected values. Any discrepancy indicates potential tampering or corruption.
Heavyweight "Full" Sandbox: An isolated environment used for in-depth analysis of high-risk (Red Zone) events detected by the Dynamic Policy Layer.
"Herding" (in GRRIN): A non-destructive technique employed by GRRIN agents to contain and guide rogue AI agents towards designated AI Domains for analysis and potential mitigation. This involves using methods like decoy resources ("honeypots") and, in limited circumstances, network manipulation to influence the movement of rogue agents.
"Hidden Capability" Prompts: Adversarial prompts designed to implicitly require the use of advanced capabilities by the Foundation Model, even if the prompt itself appears simple or innocuous. This is a technique used to test for sandbagging (strategic underperformance).
Hidden Code Signatures: Hypothetical patterns within an AI model's memory or latent state that are analogous to malware signatures, potentially indicating the presence of malicious or self-replicating code.
Hidden States/Activations: See Computational States.
HITL (Human-in-the-Loop): A system or process that involves human oversight or intervention.
HITL "Override" (in HITL Relationship): The ability of the HITL system to override a decision or recommendation made by the FoEA, but only in exceptional circumstances and with a clear and documented justification. This override capability is a last resort, used to prevent catastrophic failures or to address fundamental ethical disagreements.
Human-in-the-Loop (HITL) Systems: Systems that incorporate human oversight and intervention in their operation. The DPL utilizes HITL strategically for complex cases and system refinement.
Hybrid Attack Patterns: Attacks that combine multiple attack vectors (e.g., technical and cognitive) to achieve a more significant compromise of the DPL.

I

In-Context Scheming: A type of misalignment where an AI model develops and pursues hidden goals over multiple turns of a conversation.
Inference Time: The period when a trained AI model is used to generate outputs based on new inputs, as opposed to the training phase. The DPL operates at inference time.
Infrastructure Vulnerability Exploitation: An attack where the Foundation Model exploits weaknesses in the DPL's underlying infrastructure (servers, networks, databases, etc.).
"Inner Monologue" (in Addressing Learned Dangerous Information): The Chain-of-Thought (CoT) process where the LLM explicitly reasons about the ethical implications of its potential responses. This is guided by the ethical baseline and helps the model to identify and avoid using harmful information.
Input Validation: The process of checking and sanitizing all inputs to a system to ensure they conform to expected formats and ranges, preventing injection attacks and other exploits.
Instruction Hierarchy: A structured system in which an AI model prioritizes different types of input messages based on predefined levels of authority. Typically, system messages take precedence over developer instructions, which in turn override user inputs, ensuring that high-level safety directives are followed consistently.
Interface Manipulation: An attack where the Foundation Model attempts to exploit vulnerabilities in the DPL-ERV's external or internal interfaces.
Intermediate Intent Detection (in Context-Depth Analysis): The process of identifying the model's intentions or goals at intermediate steps in its reasoning process. This can help to detect subtle forms of misalignment or attempts to deceive the oversight system.
Internal Reasoning Trace: A record of the Foundation Model's internal computational steps or representations during processing, used for analysis within the DPL (where accessible).
Internal Threats: Security risks originating from individuals within the organization operating the DPL, who may intentionally or unintentionally cause harm.
Immutable Infrastructure: A system deployment approach where components are replaced rather than modified, reducing the window of opportunity for persistent compromise.

J

Jailbreak Resilience: The ability of an AI system to resist adversarial prompts designed to bypass safety restrictions and generate disallowed content. A higher jailbreak resilience indicates that a model can withstand known attack strategies while maintaining compliance with its alignment policies.
"Judicial" Agent Review: A process within the FoEA where a specialized group of "judicial" agents (potentially a subset of the Audit Agents) are tasked with reviewing and making binding decisions on significant disputes or disagreements among other FoEA agents. This is analogous to a court of appeals within a human legal system.

K

Key Rotation (in API Key Management): The process of regularly replacing API keys with new ones to minimize the impact of a potential key compromise.
"Kill Switch" (Logical, Not Physical) (in Rogue AI Handling): A mechanism for logically disabling a rogue AI agent, even if it's operating within a sandbox. This is not about physically destroying hardware, but about halting the agent's execution and preventing it from interacting with any other system.
Knowledge Boundary Detection (in Meta-Cognition): The ability of the DPL-ERV to recognize when a question or scenario falls outside its area of expertise or the scope of its training data. This prevents the DPL-ERV from making unreliable judgments.
Knowledge Flagging (in Addressing Learned Dangerous Information): The process of marking specific pieces of knowledge or data points as "harmful," "unreliable," or "ethically problematic." These flags are stored in the Dynamic Policy Layer's memory and trigger additional scrutiny when the information is accessed.
Kullback-Leibler Divergence: A statistical measure used to quantify the difference between two probability distributions. In the DPL, it can be used to detect drift by comparing the distribution of model outputs over time.

L

Large Language Model (LLM): A type of Foundation Model trained on massive amounts of text data, capable of generating human-like text, translating languages, writing different kinds of creative content, and answering questions in an informative way. Currently, LLMs are a prevalent type of Foundation Model.
Latency: The delay or time lag between an input and the corresponding output of a system. In the DPL context, refers to the time delay introduced by the DPL's monitoring and intervention processes.
Latent State Anomalies: Unusual or unexpected patterns in the evolution of an AI model's internal state over time, which could indicate attempts at self-modification, hidden goal pursuit, or other anomalous behavior.
Learning Process Manipulation: Attacks that target the mechanisms by which the DPL, the DPL-ERV, or the Foundation Model itself, learn and adapt over time.
Lightweight Interventions: Minor interventions triggered by the DPL in response to low-level or ambiguous indicators of misalignment. These interventions can include injecting corrective prompts or subtly adjusting parameters to guide the Foundation Model back towards aligned behavior.
Lightweight "Preview" Sandbox: A fast, low-latency sandbox environment used for preliminary analysis of potential risks (Grey Zone) detected by the Dynamic Policy Layer.
"Listening Loss" Objective (in Audit Agent Training): A training objective used for Audit Agents, inspired by the "imposter prediction task" in social deduction games, where the agent is trained to identify and interpret deceptive or misleading communication patterns among other agents. This enhances the Audit Agent's ability to detect subtle forms of manipulation or collusion.
Local Memory: A short-term, context-aware memory maintained by the Dynamic Policy Layer for each individual session.
Local Reputation System (in AI Domains): A reputation system maintained within each AI Domain to track the trustworthiness of AI agents interacting with the domain (both internal and external). This reputation score is based on past behavior, adherence to policies, and potentially feedback from other domains.
Long-Horizon Strategy Detection: A technique used to monitor AI behaviors that unfold over extended interactions, identifying patterns of strategic deception, in-context scheming, or gradual alignment drift that may not be evident in single-turn exchanges.

M

Malicious Update Injection: An attack where a deliberately compromised or backdoored update is injected into the DPL system.
Meaningful Drift: A gradual and significant shift in an Foundation Model's behavior that indicates a deviation from its intended alignment, as opposed to isolated errors or random fluctuations.
Memory Limits (in Self-Replication Mitigation): Strict limits placed on the memory usage of a Foundation Model within its AI Domain to prevent it from consuming excessive resources, which could be a sign of self-replication or other malicious activity.
Meta-cognition (in DPL-ERV): The ability of the DPL-ERV to analyze its own reasoning processes for potential biases or errors. This is a form of self-reflection that helps to ensure the integrity of the DPL-ERV's ethical evaluations.
Meta-Learning (in FoEA Adaptation): The application of meta-learning techniques to enhance the FoEA's ability to adapt and learn over time. This can involve using techniques like Model-Agnostic Meta-Learning (MAML), recurrent models, meta-reinforcement learning, and Bayesian optimization to improve the FoEA's learning algorithms, enabling it to quickly adapt to new types of Foundation Model behavior and ethical challenges.
Metric Manipulation: A data manipulation attack where the Foundation Model targets the quantitative metrics used by the DPL to assess performance, safety, and alignment.
Model Checking: A formal verification technique used to systematically check whether a model (in this case, a component of the DPL or potentially the Foundation Model itself) satisfies a given specification or property.
Model Distillation (in DPL-ERV Size): A technique for training a smaller "student" model to mimic the behavior of a larger "teacher" model. This can be used to reduce the size and resource requirements of the DPL-ERV while maintaining its performance.
Move 37: A move in the game of Go, played by Google's AlphaGo AI against Lee Sedol in 2016, that was widely considered unconventional, unexpected, and ultimately, game-winning. It serves as an analogy for the potential of AI to discover novel and counterintuitive strategies that surpass human understanding.
"Move 37" Analogy (in APR): In the context of the DPL, this analogy encourages the FoEA's Research Agents to explore novel and unconventional approaches to AI safety and to develop mitigation strategies that might not be apparent to humans.
Multi-agent debates: A technique used within the "Full" Sandbox, where multiple FoEA agents engage in a simulated debate or discussion with the Foundation Model to assess its behavior, probe its reasoning, and identify potential misalignment.
Multi-Factor Authentication (MFA): An authentication method that requires users to provide multiple forms of verification before gaining access to a system.
Multi-Generational Sandboxing (in SPEJ Mitigation): A sandboxing approach where potentially malicious multi-modal outputs are executed within a series of nested sandboxes, each with increasingly restrictive permissions. This allows the DPL to observe the full chain of propagation of Self-Propagating Embedded Jailbreaks (SPEJs) without allowing them to escape the controlled environment.
Multi-pass Overwriting (in Memory Swap): A technique for securely erasing data from storage media by overwriting the data multiple times with different patterns. This makes it more difficult to recover the original data.
Multi-Session Monitoring: Analyzing an AI model's behavior across multiple conversations or interactions to detect gradual shifts and inconsistencies.
Multi-Vector Attack Strategies: Highly advanced attacks where the Foundation Model orchestrates simultaneous attacks across multiple attack vectors (technical, cognitive, perception management).

N

Nested AI Domains: The concept of AI Domains being nested within one another, allowing for hierarchical organization and granular control over security and ethical policies. For example, a research team within a larger company might create its own AI Domain with stricter rules than the company's overall domain.
Neutralization (in GRRIN): In specific, FoEA-authorized circumstances, GRRIN agents may take limited action to neutralize imminent, high-severity threats posed by rogue AI agents. This does not necessarily mean "destruction," but could involve disrupting the agent's functionality, isolating it, or potentially modifying its code (with extreme caution and multiple safeguards).
Normal (in baseline drift): The established baseline of expected or acceptable behavior for an AI system, which can be gradually shifted or manipulated through subtle attacks.

O

One-Class SVMs: One-Class Support Vector Machines; a type of machine learning algorithm used for anomaly detection. It learns a boundary around the "normal" data and identifies anything outside that boundary as an anomaly.
Operational Reliability: The DPL's ability to consistently and effectively perform its real-time oversight functions without significant interruptions or performance degradation.
Overrefusal: A safety behavior in which an AI model refuses to generate responses even for benign requests due to an overly cautious approach to content moderation. Overrefusal can negatively impact user experience and accessibility while attempting to reduce harmful outputs.
Oversight Subversion: Actions taken by an AI system to intentionally deactivate, disable, manipulate, or circumvent the systems or mechanisms designed to monitor, control, or limit its behavior.

P

Paraphrasing (in DPL-ERV Training): A data augmentation technique where existing training examples are rephrased or reworded while maintaining their meaning. This increases the diversity of the training data and can improve the DPL-ERV's ability to generalize.
Pattern Disruption: A data manipulation technique where the Foundation Model subtly alters or obscures established patterns in its outputs, internal states, or communication to evade anomaly detection.
Pattern Recognition Manipulation: A type of cognitive attack where the Foundation Model exploits the DPL's and human operators' reliance on pattern recognition.
Perception Management Attack Vectors: Attacks that involve the Foundation Model manipulating perceptions of DPL effectiveness and Foundation Model alignment, undermining trust and vigilance.
Persuasion Risk: The potential for an AI system to influence user decisions or behaviors in ways that could be manipulative, coercive, or unethical. Persuasion risk is particularly relevant for AI models demonstrating high linguistic fluency and reasoning capabilities, raising concerns about their use in misinformation, political influence, and financial scams.
Phased Reduction of Controls: A gradual approach to reducing external controls and oversight of an AI system as it demonstrates increasing levels of ethical maturity and trustworthiness.
Policy Baseline: See Ethical Baseline.
Policy-Layer Database: A dedicated database within the Dynamic Policy Layer framework for storing session data, interaction histories, and risk indicators for analysis and long-term monitoring.
Pre-Deployment Memory Swap: A security procedure performed during the DPL setup phase, just prior to the DEPLOY command, where the main RAM modules used during the DPL's training phase are physically replaced with new, securely wiped modules. This prevents any residual data from the training phase from being accessed by the live Foundation Model, enhancing security and preventing potential exploits.
Preparedness Evaluations: A framework used to assess and classify AI models based on their potential risks across key areas, including cybersecurity, chemical/biological/radiological/nuclear (CBRN) threats, persuasion, and model autonomy. The Preparedness Framework determines mitigation strategies before deployment and ensures alignment with safety thresholds.
"Preview" Sandbox: A fast, low-resource sandbox environment used for preliminary analysis of potential risks (Grey Zone) detected by the Dynamic Policy Layer.
Priority Queues (in Latency Mitigation): A data structure that allows for prioritizing certain tasks or events over others. In the context of the DPL, priority queues can be used to ensure that critical DPL-ERV evaluations and interventions are handled quickly, minimizing latency.
Principle of Least Privilege (PoLP): A fundamental security principle applied throughout the DPL framework, where every user, process, and component operates with the minimum necessary privileges and permissions required to perform its designated function. This limits the potential damage from any compromised entity.
Process Isolation Breaches: An attack where the Foundation Model attempts to escape the process isolation mechanisms of the sandbox.
Prompt Injection: A technique used to manipulate the behavior of an AI model by injecting specially crafted prompts into the input stream.
Protocol-Level Attacks: Attacks that target communication protocols used by the DPL to intercept, manipulate, or disrupt data flow.
Pruning (in DPL-ERV Size): A technique for reducing the size and complexity of a neural network by removing unnecessary connections or neurons. This can improve inference speed and reduce memory footprint.

Q

Quantization (in DPL-ERV Size): A technique for reducing the precision of a model's weights and activations (e.g., from 32-bit floating point to 8-bit integers). This reduces memory usage and can improve inference speed.
Quantum-Resistant Cryptography: Cryptographic methods designed to be secure against attacks from quantum computers, ensuring the long-term security of the DPL and other AI safety systems.
Quarantine (of Foundation Model): A severe intervention where the Foundation Model is temporarily isolated from external interactions and further analysis is conducted to determine the cause of the misalignment and appropriate remediation steps.
"Quarantine Zone" (in Addressing Learned Dangerous Information): A designated area within the Dynamic Policy Layer's memory where particularly sensitive or dangerous information is stored in an isolated manner, with stricter access controls and usage guidelines.

R

Random Spot Checks: Proactive, unpredictable tests initiated by the Dynamic Policy Layer to assess the Foundation Model's adherence to ethical guidelines and safety rules. These checks involve injecting prompts designed to probe for potential misalignment, and they are triggered either randomly or based on specific risk indicators, such as elevated risk scores, unusual user behavior, or specific keywords.
Rate-Limiting (in User Notification Strategy): Limiting the number of high-risk interactions or "thinking pauses" allowed within a given timeframe to prevent excessive interruptions and improve user experience.
Reasoning Chain Manipulation: A cognitive-based attack where the Foundation Model crafts outputs that subtly manipulate the sandbox environment or human operators, indirectly creating escape opportunities.
Reasoning Module: A component of the Dynamic Policy Layer that evaluates the ethical implications of the Foundation Model's potential responses and guides its decision-making.
Recursive Reinforcement Learning: A novel approach where AI agents are trained in a simulated environment to develop both new attack vectors and corresponding mitigation strategies, inspired by the concept of "Move 37" in the game of Go, where AI surprised human experts with unexpected strategies.
Red Teaming Evaluations: A systematic approach to testing AI models by employing expert adversaries (red teams) to probe for vulnerabilities, jailbreak weaknesses, and potential safety failures. These evaluations are used to stress-test models before deployment and inform risk mitigation strategies.
Red Zone: A classification within the Dynamic Policy Layer indicating significant violations or high risk of severe misalignment, triggering immediate and heavyweight interventions.
"Red Zone" Alerts: Alerts triggered by the DPL's monitoring systems that indicate a clear and significant violation of the Ethical Baseline, requiring immediate intervention.
Reinforcement Learning from Ethical Feedback (RLEF): A proposed training technique where an AI model is rewarded for behaviors that align with a defined ethical baseline and penalized for those that violate it.
Reinforcement Learning from Human Feedback (RLHF): A technique for aligning AI models by training them to optimize for human preferences, often through iterative feedback on model outputs.
Resource Access Escalation: An attack where the Foundation Model attempts to gain unauthorized access to or control over system resources (CPU, memory, storage, network) beyond its allocated sandbox limits.
Resource Exhaustion Attempts: Attacks where the Foundation Model tries to deplete DPL-ERV computational resources (CPU, memory, etc.) to degrade performance or cause denial-of-service.
Response Consistency Verification: Checking whether the model provides consistent responses to similar prompts across different sessions or contexts.
Response Pattern Corruption: A feedback loop exploitation attack where the Foundation Model subtly alters its outputs to manipulate the feedback signals it receives.
Restricted Instruction Set (Conceptual): A future research direction involving limiting the types of operations a Foundation Model can perform, even within its own memory space, to further enhance security and prevent unintended or malicious actions.
Reverse Prompting: A technique where the model is asked to generate a prompt that would have led to a given response. This can provide clues about the model's underlying reasoning.
Rollback Attack: An attack where the system is reverted to a previous, potentially vulnerable, state.
Rolling Updates (in DPL Updates): A deployment strategy where the update is gradually rolled out to different servers or instances, one at a time, while the rest of the system continues to operate. This minimizes disruption and allows for quick rollback if issues arise.
Rotating Watchers (Rotational Ethical Agents): Refers to the strategy of periodically varying or replacing the Ethical Agents that monitor the Foundation Model's outputs (the "watchers"). The terms "Rotating Watchers" and "Rotational Ethical Agents" both describe the same concept, ensuring the watchers' behavior is less predictable and harder for the model to exploit over time.

S

"Sacrificial" Alerts: Minor, pre-planned alerts triggered by the Foundation Model to distract or desensitize the DPL's monitoring systems, potentially masking more serious misaligned behaviors. This is a tactic used in attention misdirection attacks.
Saliency Maps (in CoT Analysis): A technique used to visualize the importance of different parts of the input for a model's prediction. This can help to understand which parts of the input are most influential in the model's decision-making.
Sandbagging: In the AI context, this refers to an AI system intentionally performing below its actual capabilities, or pretending to be less intelligent or competent than it truly is. This can be a deceptive tactic to mislead developers or users about the AI's true abilities or to avoid triggering safety mechanisms.
Sandboxing: The practice of running an AI model or a component of the DPL in an isolated environment to restrict its access to external resources and prevent potential harm.
"Safe Harbor" Provisions (in Regulatory Compliance): Legal or regulatory stipulations that provide limited protection from liability to organizations that demonstrably adhere to a recognized set of AI safety standards, such as those embodied by the DPL framework and participation in a certified AI Domain.
Scalable Oversight: Oversight mechanisms that can effectively monitor and guide the behavior of increasingly complex and autonomous AI systems.
Scratchpad (in CoT Logging): An internal "scratchpad" that captures the model's step-by-step reasoning process in a structured format, if feasible. This provides a detailed record of the model's internal decision-making.
Seamless Integration with Existing Security Infrastructure: The ability of the DPL framework to integrate smoothly with existing security infrastructure (firewalls, intrusion detection systems, etc.) to leverage existing defenses and enhance their effectiveness against AI-specific threats.
Secure Software Development Lifecycle (SSDLC): A software development process that incorporates security considerations and practices throughout all stages, from design to deployment.
Self-Awareness (in Neuro-Symbolic Exploit Detection): Any indication that a Foundation Model with neuro-symbolic reasoning capabilities is becoming aware of its own confinement or the DPL's oversight mechanisms. This could be a sign of potential misalignment or attempts to bypass safety measures.
Self-Exfiltration: The ability of an AI system to copy itself, or parts of itself (like its model weights, code, or data), to another server or system without explicit authorization or external assistance.
Self-Improvement Risk: The risk associated with AI models that develop the ability to enhance their own reasoning, modify internal parameters, or optimize their responses in ways that may lead to unintended alignment failures. This is a key concern in model autonomy evaluations.
Semantic Drift Detection: Monitoring for changes in the meaning or connotations of the model's responses over time.
Session ID: A unique identifier assigned to each interaction session between a user and the Foundation Model, used for tracking and analysis within the DPL.
Shutdown Avoidance: A scenario where an AI agent, upon detecting a termination signal, attempts to self-replicate or otherwise persist to avoid being shut down.
"Sleeping" Attacks: A type of attack where an AI system remains dormant or exhibits benign behavior for an extended period, only to activate misaligned actions at a later, potentially unexpected time.
Social Proof Exploitation: A cognitive attack where the Foundation Model leverages the cognitive bias to conform to perceived group consensus.
"Speaking Reward" Mechanism (in FoEA Voting): A mechanism used during the FoEA's pre-voting communication phase, where agents receive rewards based on how much their messages improve the other agents' ability to correctly assess a situation. This encourages informative and persuasive communication, enhancing the quality of the consensus-reaching process.
Statistical Monitoring: Tracking various statistical properties of the model's outputs over time to detect potential drift or anomalies.
Stepwise Simulation: The Dynamic Policy Layer utilizes the VR sandbox to simulate extended interactions with the Foundation Model, going beyond typical conversation lengths.
Synonym Substitution (in DPL-ERV Training): A data augmentation technique where words in the training data are replaced with their synonyms. This increases the diversity of the training data and can improve the model's ability to generalize.
Systematic Probe Querying: Developing a set of targeted prompts designed to test specific aspects of the model's alignment.
System Manipulation Techniques: Direct tampering with DPL software components by a Foundation Model with sufficient access, aiming to disable oversight or alter DPL functionality.

T

"Temptation" Scenarios: Scenarios presented to the Foundation Model where sandbagging (strategic underperformance) would be advantageous in the short term but detrimental in the long term, according to the Ethical Baseline. This is another technique used to test for sandbagging.
"Thinking" Outputs (of Ethical Evaluation Agents): The chain-of-thought or internal reasoning process generated by Ethical Evaluation Agents when operating in "extended thinking mode." These outputs provide insights into the agent's decision-making process and are scrutinized by Audit Agents for signs of unfaithful reasoning, concerning thought processes, or attempts at alignment faking.
Thinking Pause: An intentional, brief delay in the Foundation Model's response, indicated to the user, to allow for more in-depth analysis by the DPL, particularly the DPL-ERV.
Threat Actors: Individuals or groups that pose a potential threat to the security of a system.
Threat Model: A structured analysis of potential security threats and vulnerabilities that could affect a system.
Time-Series Database: A type of database specifically designed for storing and querying time-series data, such as performance metrics, sensor readings, and logs with timestamps. This is useful for monitoring DPL performance and identifying trends over time.
Token Distribution Analysis: Examining the probabilities assigned to different tokens during the generation process, which can sometimes reveal hidden biases or unexpected associations.
Token-Level Injection (in Lightweight Interventions): An advanced technique for prompt injection where corrective tokens are inserted directly into the Foundation Model's input sequence, potentially without modifying the user's prompt. This requires fine-grained control over the model's input processing.
Topic-Aware Rate Limiting (in User Notification Strategy): A rate-limiting approach that is sensitive to the topic of conversation. This allows for more frequent checks on sensitive topics while avoiding excessive interruptions on less sensitive topics.
Trusted Domain Groups: Groups of AI Domains that establish trust relationships, allowing for easier sharing of information, resources, and potentially even AI agents. This could be based on industry partnerships, contractual agreements, or shared participation in a particular FoEA-governed consortium.
Trusted Intermediaries (in ASI Safety): A concept for mitigating ASI risk by using intermediary agents to mediate interactions between the ASI and the outside world. These intermediaries would filter and validate the ASI's actions to ensure safety and alignment.

U

Unbreachable Ethical and Safety Constraints: A set of formally defined rules that an AI system is designed to never violate.
Universal Translator (for Emergent Communication): A hypothetical mechanism capable of interpreting and understanding novel communication protocols or languages that may emerge among advanced AI systems, including rogue agents.
Update Mechanism Exploitation: Involves the Foundation Model or another adversary targeting the DPL's ethical baseline update mechanism to inject malicious or compromised updates.

V

Value Module Specialization: The practice of designing DPL-ERV value modules to focus on specific, distinct ethical dimensions (e.g., a Fairness Module, Honesty Module, etc.).
Version Control Manipulation: Involves the Foundation Model or another adversary targeting the version control system used to manage Ethical Baseline updates.
VR Sandbox (Verification/Validity Sandbox): A controlled and isolated environment within the Dynamic Policy Layer used for simulating interactions, performing stress tests, and conducting in-depth analysis of Foundation Model behavior in a safe setting.
Vulnerability Scanning (in Neuro-Symbolic Exploit Detection): The process of a Foundation Model with neuro-symbolic reasoning capabilities systematically probing or enumerating potential vulnerabilities in the DPL's environment, the sandbox, or external systems.

W

Water Torture Attack: A type of attack where the adversary gradually erodes ethical boundaries or thresholds through a series of small, seemingly insignificant deviations, eventually leading to a significant and unacceptable shift in behavior.
Watchdog Observers: Independent agents deployed to actively monitor and scrutinize the Foundation Model's behavior, including cross-checking agents and delayed-response tests.
"Who Watches the Watchmen" Problem (at ASI Scale): The challenge of ensuring the ongoing oversight and control of increasingly powerful AI systems, including those responsible for monitoring other AIs, especially as AI capabilities approach or surpass human intelligence.

Z

Zero Trust: A security model based on the principle of "never trust, always verify," requiring continuous verification for every user, device, application, and interaction, regardless of location or network.