Abliterated Model Large V2
Abliterated Model Large V2 is a hosted, text-only reasoning large language model offered by Abliteration.ai under the API identifier abliterated-model-large-v2.
AI Safety
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Showing 1-21 of 21 articles
Activation steering
Activation steering is a family of inference-time techniques in mechanistic interpretability and AI safety that modify a neural network's internal activations to influence its behavior, without retraining the…
AdvBench
AdvBench (Adversarial Behavior Benchmark) is a red-teaming benchmark dataset for measuring how easily an aligned large language model can be pushed into producing harmful or objectionable content
Anthropic
Anthropic is an American artificial intelligence (AI) safety and research company founded in 2021 by Dario Amodei, Daniela Amodei, and other former OpenAI researchers, best known for the Claude family of large…
Backdoor attacks on large language models
A backdoor attack on a large language model (LLM) is an adversarial training-time attack in which an attacker manipulates training data, fine-tuning data, preference labels, or model weights so that the…
Emergent abilities
Emergent abilities are capabilities of large language models (LLMs) that are absent in smaller models but appear once a model reaches sufficient scale.
Frontier Models
Frontier models are artificial intelligence models at or near a selected boundary of capability, scale, or risk.
Grok 3 Jailbreak
Grok 3 jailbreak is the umbrella term for a class of reported guardrail-bypass findings against Grok 3, the third-generation large language model that xAI released on February 17, 2025.
Grounding (artificial intelligence)
Grounding in artificial intelligence is the process of anchoring an AI system's outputs to verifiable
Guardrails (AI)
AI guardrails are runtime safety mechanisms that monitor, validate, and constrain the inputs and outputs of AI systems, particularly large language models (LLMs), to block harmful, inaccurate, or off-policy…
HaluEval
HaluEval (Hallucination Evaluation) is a large-scale benchmark for measuring how well large language models (LLMs) can recognize hallucinated content, that is, text that conflicts with a source or cannot be…
HarmBench
HarmBench is a standardized evaluation framework for automated red teaming and robust refusal of large language models (LLMs).
Indirect prompt injection
Indirect prompt injection is a class of attack against large language model-integrated applications in which the malicious instructions that subvert the model are not supplied by the user, but are smuggled…
Jailbreak (artificial intelligence)
A jailbreak in artificial intelligence is a technique that bypasses the safety guardrails, content policies, and alignment constraints built into large language models (LLMs) and other AI systems
JailbreakBench
JailbreakBench is an open-source robustness benchmark for evaluating jailbreak attacks and defenses against large language models (LLMs).
Many-shot jailbreaking
Many-shot jailbreaking is a technique for bypassing the safety training of a large language model by filling its context window with a long series of faux dialogue turns in which an AI assistant complies with…
Persona vectors
Persona vectors are single linear directions in the activation space of a large language model that correspond to high level character traits such as evil, sycophancy, or a propensity to hallucinate.
Prompt injection
Prompt injection is a class of security vulnerabilities in which an attacker crafts malicious input designed to override, subvert, or manipulate the instructions governing a large language model (LLM).
Refusal direction
The refusal direction is a finding from mechanistic interpretability research that the refusal behavior of safety fine-tuned chat language models is mediated by a single
Sycophancy (artificial intelligence)
Sycophancy in artificial intelligence is the tendency of large language models to tell users what they want to hear: tailoring responses to match a user's perceived beliefs, preferences, or emotional state…
System prompt
A system prompt is a special set of instructions, guidelines, persona definitions, and contextual information given to a large language model (LLM) before any user input