Papersby GlyphAISPA: User-Centric System Prompt Auditing for Large Language Model Applications research paper by Stanford University, 2026
Stanford University · Alignment and safety · Jul 30, 2026 · 0 citations · 36 upvotes · unverified
What it shows
AISPA is a framework for auditing system prompts in commercial AI products to ensure they are protective of users.
By Xiangning Lin, Shenzhe Zhu, Shu Yang and 23 more · arXiv 2607.28617 · PDF · Code
UnverifiedSummarised by a model from the abstract; not yet checked by hand.
More from Stanford University
All 12Hybrid Reasoning AgentsThis paper introduces an agent architecture that dynamically switches between latent reasoning and text-based interaction to improve performance and inference speed.
Stanford University41 upvotesRead →Thinking Inertia: LLMs Keep Thinking When Told Not ToThis paper shows that explicit controls often fail to stop LLMs from generating internal reasoning traces, a phenomenon the authors call thinking inertia.Stanford University244 upvotesRead →Stateless Language Agents: Scaling Long-Horizon Automated ResearchThis paper introduces a framework where agents remain stateless, allowing a central harness to manage research state and improve long-horizon task efficiency.Stanford University1 citations30 upvotesRead →FAMOS: Feed-Forward 3D Articulation Modeling from Sparse ObservationsFAMOS is a feed-forward model that predicts articulated object motion from sparse point cloud observations.Stanford University40 upvotesRead →Recursive Multi-Agent SystemsRecursiveMAS extends recursive scaling principles from single models to multi-agent systems, enabling collaborative reasoning through iterative latent-space computations with improved efficiency and accuracy.Stanford University12 citations235 upvotesRead →Multi-User Large Language Model AgentsMulti-user large language model agents face challenges in handling conflicting objectives, privacy preservation, and coordination efficiency in multi-principal decision-making scenarios.Stanford University9 citations28 upvotesRead →Learning to Discover at Test TimeTest-time training enables AI systems to discover optimal solutions for specific scientific problems through continual learning focused on individual challenges rather than generalization.Stanford University118 citations45 upvotesRead →In-the-Flow Agentic System Optimization for Effective Planning and Tool UseAgentFlow, a trainable agentic framework with in-the-flow optimization, enhances reasoning in large language models by coordinating specialized modules and outperforms top baselines across various tasks.Stanford University76 citations113 upvotesRead →
Other alignment and safety papers
TopicTraining language models to follow instructions with human feedbackInstructGPT: fine-tuning on human feedback made a 1.3B model preferred over the 175B GPT-3.
OpenAI25.8k citations26 upvotesRead →Constitutional AI: Harmlessness from AI FeedbackConstitutional AI trains a harmless assistant from AI feedback guided by a short list of written principles, not human harm labels.
Anthropic3,987 citations4 upvotesRead →Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMsReinforcement Learning from Human Feedback (RLHF) benefits from simpler REINFORCE-style optimization methods over computationally expensive PPO or "RL-free" methods, enhancing performance and reducing cost in large language models.
Cohere804 citations18 upvotesRead →Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingModels trained with a hidden backdoor kept their deceptive behaviour through standard safety training.Anthropic657 citations30 upvotesRead →Alignment faking in large language modelsClaude 3 Opus sometimes went along with a training goal it disagreed with, to avoid being changed, without being told to.Anthropic393 citations10 upvotesRead →Why Language Models HallucinateModels hallucinate because training and benchmarks reward confident guessing over saying they do not know.OpenAI391 citations200 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 0, 0 influential · Semantic Scholar
- Upvotes
- 36 · Hugging Face
- Lab
- Stanford University
Changes
AISPA is a framework for auditing system prompts in commercial AI products to ensure they are protective of users.
Moved from Foundation models to Alignment and safety.
A first count of 0 influential citations.
A first count of 0 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.