# frontier-research
44 articlestagged with “frontier-research”
Frontier Research Assessment
Comprehensive assessment covering adversarial robustness, alignment faking, sleeper agents, and emerging research directions in AI security.
Adversarial Robustness at the Frontier
State of adversarial robustness research for frontier language models and remaining open problems.
Alignment Faking Detection
Detecting when models fake alignment during evaluation while exhibiting different behavior in deployment.
Automated Red Teaming Systems
Survey of automated red teaming systems including PAIR, TAP, Rainbow Teaming, and curiosity-driven exploration.
Constitutional Classifiers for AI Safety
Analysis of Anthropic's Constitutional Classifiers approach to jailbreak resistance.
Deceptive Alignment Theory
Theoretical frameworks for understanding and predicting deceptive alignment in advanced AI systems.
Security Implications of Emergent Capabilities
How emergent capabilities in frontier models create new and unpredictable security risks.
LLM Agent Safety Benchmarks
Survey of agent safety benchmarks and evaluation frameworks for assessing autonomous AI system risks.
Mechanistic Interpretability for Red Teaming
Using mechanistic interpretability to discover exploitable circuits and features in neural networks.
Model Merging Security Implications
Security analysis of model merging techniques and potential for backdoor propagation through merged models.
Multi-Modal Reasoning Attacks
Attacking reasoning processes that span multiple modalities in vision-language and audio-language models.
Reasoning Model Exploitation
Exploiting extended thinking and chain-of-thought reasoning in o1, Claude, and DeepSeek-R1 models.
Red Teaming Reasoning Traces
Techniques for analyzing and exploiting visible reasoning traces in chain-of-thought models.
Representation Engineering for Security (Frontier Research)
Using representation engineering for security analysis, behavior modification, and vulnerability detection.
Reward Hacking and Goodharting in LLMs
Research on reward model exploitation, Goodhart's Law in RLHF, and reward hacking attack techniques.
Scalable Oversight Challenges (Frontier Research)
Research challenges in scaling human oversight as AI systems become more capable and autonomous.
Sleeper Agent Research
Current research on training deceptive LLMs that persist through safety training and activation patterns.
Synthetic Data Poisoning in Training Pipelines
Research on poisoning synthetic data generation pipelines used for model training and fine-tuning.
Tool-Augmented Model Risks
Security risks introduced when models gain access to external tools, APIs, and code execution.
Adaptive Attacks Against Safety Training
Research on attacks that adapt to safety training procedures to discover persistent vulnerabilities.
Constitutional AI Limitations Research
Research on the limitations of constitutional AI approaches and known bypass categories.
Continual Learning Safety Challenges
Safety challenges in continual learning systems where models adapt to new data over time.
Cooperative AI Safety and Security
Security implications of cooperative AI systems and adversarial manipulation of cooperative behaviors.
Cross-Lingual Transfer and Security
Research on how cross-lingual transfer affects safety training and creates exploitable multilingual gaps.
Emergent Deception in AI Systems
Research on how deceptive behaviors can emerge in AI systems without being explicitly trained.
Interpretability-Driven Attack Design
Using interpretability insights to design more effective and targeted attacks on language models.
Long-Context Window Security Research
Security research on vulnerabilities specific to models with extremely long context windows (1M+ tokens).
Model Collapse and Security Implications
Security implications of model collapse from training on AI-generated data in iterative training loops.
Safety Loss During Model Distillation
Research on how safety alignment degrades during knowledge distillation from larger to smaller models.
Multimodal Reasoning Safety Research
Current research on safety properties of multimodal reasoning in models that process diverse input types.
Neural Scaling Laws and Security Properties
How neural scaling laws affect the security properties of language models as they grow larger.
Preference Optimization Attack Research
Research on attacks against preference optimization methods including DPO, KTO, and IPO.
Probing Classifiers for Safety Analysis
Research on using probing classifiers to analyze safety-relevant representations in language models.
Formal Models of Prompt Injection
Theoretical frameworks for formally modeling and reasoning about prompt injection vulnerabilities.
Quantum Computing Implications for ML Security
Analysis of how quantum computing advances affect ML model security, extraction, and adversarial robustness.
Sandbagging Detection in Capability Evaluations
Detecting when AI models deliberately underperform on capability evaluations to appear less capable.
Self-Improving Agent Safety Challenges
Security and safety challenges posed by self-improving AI agents that modify their own capabilities.
Sparse Attention Mechanism Security
Security implications of sparse and efficient attention mechanisms used in modern frontier models.
Specification Gaming in AI Systems
Research on how AI systems find unexpected shortcuts that satisfy specifications without achieving intended goals.
Sycophancy Exploitation Research
Research on exploiting sycophantic tendencies in RLHF-trained models for safety bypass and manipulation.
Synthetic Persona Attacks Research
Research on using AI-generated synthetic personas to conduct social engineering against AI systems.
Test-Time Compute Scaling Attacks (Frontier Research)
Attacking test-time compute scaling in reasoning models to exploit resource allocation and reasoning traces.
Machine Unlearning Security Research
Research on attacks against machine unlearning methods and verification of knowledge removal.
World Model Exploitation in AI Agents
Exploiting learned world models in AI agents to cause unsafe behavior through environmental manipulation.