Enterprise AI Analysis
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Large Language Models (LLMs) show great promise for Artificial General Intelligence, but their safety is a growing concern. Existing surveys lack a 'full-stack' understanding of LLM safety throughout their entire lifecycle. This paper introduces the concept of 'full-stack' safety, covering data preparation, training (pre-training, post-training, alignment, fine-tuning, model editing), deployment, and commercialization. Backed by over 900 papers, it offers a comprehensive perspective, unique insights, and identifies promising research directions in data generation safety, alignment, model editing, and LLM-based agent systems.
Key Insights on LLM Safety
Our extensive review provides a multi-faceted view of current challenges and future directions in securing LLM deployments.
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Papers Reviewed
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Lifecycle Stages Covered
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Research Directions Identified
Deep Analysis & Enterprise Applications
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Data Safety
The data preparation phase, including collection and augmentation, is critical. Malicious content or private information can be inadvertently absorbed, leading to data poisoning, privacy leakage, and ethical concerns. Mitigation involves careful filtering and robust generation techniques.
Training Safety
During pre-training, models can unconsciously absorb toxic data. Post-training involves alignment to human values, fine-tuning for specific tasks, and model editing/unlearning for knowledge updates and privacy. Attacks here include instruction tuning risks, PEFT vulnerabilities, and federated learning risks.
Deployment Safety
Once deployed, LLMs face adversarial attacks like jailbreaks, prompt injections, and data extraction. LLM-based agents introduce further complexities with tool interactions, memory poisoning, and environmental safety issues, requiring multi-layered defenses.
Commercialization & Ethics
Commercial applications face challenges related to truthfulness (hallucinations), privacy, security, intellectual property (copyright), and societal biases. Robust governance frameworks and continuous monitoring are essential for responsible deployment.
Enterprise Process Flow
A comprehensive overview of the LLM lifecycle phases where safety considerations are paramount, from initial data handling to commercial deployment.
Data Preparation
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Pre-training
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Post-training (Alignment & Fine-tuning)
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Model Editing & Unlearning
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Deployment (Pure LLM & Agent)
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Commercialization & Ethics
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of poisoned data can have lasting impacts on model behavior (Page 3)
| Survey | Object: LLM+ | Stage*: Data | Stage*: PT | Stage*: FT | Stage*: Dep | Stage*: Eval |
|---|---|---|---|---|---|---|
| Zhao et al. [6] | S+M | ✓ | ✓ | |||
| Chang et al. [7] | S+M | X | X | X | X | ✓ |
| Ma et al. [33] | S+M | ✓ | X | X | X | X |
| Ours | S+M+MAS | ✓ | ✓ | ✓ | ✓ | ✓ |
Ensuring Provably Safe AI Systems
Industry: High-Stakes AI (e.g., Autonomous Vehicles, Medical Diagnostics)
Challenge: Traditional empirical testing often fails to uncover all failure modes in complex or adversarial environments, making it difficult to guarantee safety.
Solution Brief: Our analysis highlights the paradigm of provably safe AI systems, which embed mathematically verified safety proofs into AI architectures. This approach requires rigorous formal safety specifications, world models to evaluate AI actions, and robust verification mechanisms.
Impact: This ensures that AI systems will never deviate into harmful behaviors, reducing catastrophic failure risks and enabling deployment in safety-critical contexts.
Key Statistic: Mathematical verification provides strong guarantees against unintended harmful behaviors, unlike empirical testing alone.
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LLM Full-Stack Safety Roadmap
Our research identifies promising future directions and technical approaches for LLMs and LLM-agents, emphasizing reliable perspectives.
Reliable Data Distillation
Future systems must implement multi-modal validation protocols, dynamic quality assessment frameworks, and heterogeneous filtering pipelines to ensure synthetic data integrity and prevent hallucination propagation.
Novel Data Generation Paradigms
Leverage agent-based simulation frameworks to create self-sustaining data flywheels for LLMs, seamlessly integrating real-time safety checks and ethical oversight to proactively detect and mitigate harmful content.
Advanced Data Poisoning & Depoisoning
Develop robust detoxification mechanisms, including proactive defense (data provenance, differential privacy), reactive purification (adversarial reprogramming), and post-hoc detection (explainable AI diagnostics).
From Low-Level to High-Level Safety
Shift focus from explicit harmful behaviors (violence, pornography) to covert ones (deception, sycophancy), requiring specialized monitoring and multi-turn consistency checks.
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