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-## LLM10:2025 Unbounded Consumption
-
-### Description
-
-Unbounded Consumption refers to the process where a Large Language Model (LLM) generates outputs based on input queries or prompts. Inference is a critical function of LLMs, involving the application of learned patterns and knowledge to produce relevant responses or predictions.
-
-Attacks designed to disrupt service, deplete the target's financial resources, or even steal intellectual property by cloning a model’s behavior all depend on a common class of security vulnerability in order to succeed. Unbounded Consumption occurs when a Large Language Model (LLM) application allows users to conduct excessive and uncontrolled inferences, leading to risks such as denial of service (DoS), economic losses, model theft, and service degradation. The high computational demands of LLMs, especially in cloud environments, make them vulnerable to resource exploitation and unauthorized usage.
-
-### Common Examples of Vulnerability
-
-#### 1. Variable-Length Input Flood
-  Attackers can overload the LLM with numerous inputs of varying lengths, exploiting processing inefficiencies. This can deplete resources and potentially render the system unresponsive, significantly impacting service availability.
-#### 2. Denial of Wallet (DoW)
-  By initiating a high volume of operations, attackers exploit the cost-per-use model of cloud-based AI services, leading to unsustainable financial burdens on the provider and risking financial ruin.
-#### 3. Continuous Input Overflow
-  Continuously sending inputs that exceed the LLM's context window can lead to excessive computational resource use, resulting in service degradation and operational disruptions.
-#### 4. Resource-Intensive Queries
-  Submitting unusually demanding queries involving complex sequences or intricate language patterns can drain system resources, leading to prolonged processing times and potential system failures.
-#### 5. Model Extraction via API
-  Attackers may query the model API using carefully crafted inputs and prompt injection techniques to collect sufficient outputs to replicate a partial model or create a shadow model. This not only poses risks of intellectual property theft but also undermines the integrity of the original model.
-#### 6. Functional Model Replication
-  Using the target model to generate synthetic training data can allow attackers to fine-tune another foundational model, creating a functional equivalent. This circumvents traditional query-based extraction methods, posing significant risks to proprietary models and technologies.
-#### 7. Side-Channel Attacks
-  Malicious attackers may exploit input filtering techniques of the LLM to execute side-channel attacks, harvesting model weights and architectural information. This could compromise the model's security and lead to further exploitation.
-
-### Prevention and Mitigation Strategies
-
-#### 1. Input Validation
-  Implement strict input validation to ensure that inputs do not exceed reasonable size limits.
-#### 2. Limit Exposure of Logits and Logprobs
-  Restrict or obfuscate the exposure of `logit_bias` and `logprobs` in API responses. Provide only the necessary information without revealing detailed probabilities.
-#### 3. Rate Limiting
-  Apply rate limiting and user quotas to restrict the number of requests a single source entity can make in a given time period.
-#### 4. Resource Allocation Management
-  Monitor and manage resource allocation dynamically to prevent any single user or request from consuming excessive resources.
-#### 5. Timeouts and Throttling
-  Set timeouts and throttle processing for resource-intensive operations to prevent prolonged resource consumption.
-#### 6.Sandbox Techniques
-  Restrict the LLM's access to network resources, internal services, and APIs.
-  - This is particularly significant for all common scenarios as it encompasses insider risks and threats. Furthermore, it governs the extent of access the LLM application has to data and resources, thereby serving as a crucial control mechanism to mitigate or prevent side-channel attacks.
-#### 7. Comprehensive Logging, Monitoring and Anomaly Detection
-  Continuously monitor resource usage and implement logging to detect and respond to unusual patterns of resource consumption.
-#### 8. Watermarking
-  Implement watermarking frameworks to embed and detect unauthorized use of LLM outputs.
-#### 9. Graceful Degradation
-  Design the system to degrade gracefully under heavy load, maintaining partial functionality rather than complete failure.
-#### 10. Limit Queued Actions and Scale Robustly
-  Implement restrictions on the number of queued actions and total actions, while incorporating dynamic scaling and load balancing to handle varying demands and ensure consistent system performance.
-#### 11. Adversarial Robustness Training
-  Train models to detect and mitigate adversarial queries and extraction attempts.
-#### 12. Glitch Token Filtering
-  Build lists of known glitch tokens and scan output before adding it to the model’s context window.
-#### 13. Access Controls
-  Implement strong access controls, including role-based access control (RBAC) and the principle of least privilege, to limit unauthorized access to LLM model repositories and training environments.
-#### 14. Centralized ML Model Inventory
-  Use a centralized ML model inventory or registry for models used in production, ensuring proper governance and access control.
-#### 15. Automated MLOps Deployment
-  Implement automated MLOps deployment with governance, tracking, and approval workflows to tighten access and deployment controls within the infrastructure.
-
-### Example Attack Scenarios
-
-#### Scenario #1: Uncontrolled Input Size
-  An attacker submits an unusually large input to an LLM application that processes text data, resulting in excessive memory usage and CPU load, potentially crashing the system or significantly slowing down the service.
-#### Scenario #2: Repeated Requests
-  An attacker transmits a high volume of requests to the LLM API, causing excessive consumption of computational resources and making the service unavailable to legitimate users.
-#### Scenario #3: Resource-Intensive Queries
-  An attacker crafts specific inputs designed to trigger the LLM's most computationally expensive processes, leading to prolonged CPU usage and potential system failure.
-#### Scenario #4: Denial of Wallet (DoW)
-  An attacker generates excessive operations to exploit the pay-per-use model of cloud-based AI services, causing unsustainable costs for the service provider.
-#### Scenario #5: Functional Model Replication
-  An attacker uses the LLM's API to generate synthetic training data and fine-tunes another model, creating a functional equivalent and bypassing traditional model extraction limitations.
-#### Scenario #6: Bypassing System Input Filtering
-  A malicious attacker bypasses input filtering techniques and preambles of the LLM to perform a side-channel attack and retrieve model information to a remote controlled resource under their control.
-
-### Reference Links
-
-1. [Proof Pudding (CVE-2019-20634)](https://avidml.org/database/avid-2023-v009/) **AVID** (`moohax` & `monoxgas`)
-2. [arXiv:2403.06634 Stealing Part of a Production Language Model](https://arxiv.org/abs/2403.06634) **arXiv**
-3. [Runaway LLaMA | How Meta's LLaMA NLP model leaked](https://www.deeplearning.ai/the-batch/how-metas-llama-nlp-model-leaked/): **Deep Learning Blog**
-4. [I Know What You See:](https://arxiv.org/pdf/1803.05847.pdf): **Arxiv White Paper**
-5. [A Comprehensive Defense Framework Against Model Extraction Attacks](https://ieeexplore.ieee.org/document/10080996): **IEEE**
-6. [Alpaca: A Strong, Replicable Instruction-Following Model](https://crfm.stanford.edu/2023/03/13/alpaca.html): **Stanford Center on Research for Foundation Models (CRFM)**
-7. [How Watermarking Can Help Mitigate The Potential Risks Of LLMs?](https://www.kdnuggets.com/2023/03/watermarking-help-mitigate-potential-risks-llms.html): **KD Nuggets**
-8. [Securing AI Model Weights Preventing Theft and Misuse of Frontier Models](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2800/RRA2849-1/RAND_RRA2849-1.pdf)
-9. [Sponge Examples: Energy-Latency Attacks on Neural Networks: Arxiv White Paper](https://arxiv.org/abs/2006.03463) **arXiv**
-10. [Sourcegraph Security Incident on API Limits Manipulation and DoS Attack](https://about.sourcegraph.com/blog/security-update-august-2023) **Sourcegraph**
-
-### Related Frameworks and Taxonomies
-
-Refer to this section for comprehensive information, scenarios strategies relating to infrastructure deployment, applied environment controls and other best practices.
-
-- [MITRE CWE-400: Uncontrolled Resource Consumption](https://cwe.mitre.org/data/definitions/400.html) **MITRE Common Weakness Enumeration**
-- [AML.TA0000 ML Model Access: Mitre ATLAS](https://atlas.mitre.org/tactics/AML.TA0000) & [AML.T0024 Exfiltration via ML Inference API](https://atlas.mitre.org/techniques/AML.T0024) **MITRE ATLAS**
-- [AML.T0029 - Denial of ML Service](https://atlas.mitre.org/techniques/AML.T0029) **MITRE ATLAS**
-- [AML.T0034 - Cost Harvesting](https://atlas.mitre.org/techniques/AML.T0034) **MITRE ATLAS**
-- [AML.T0025 - Exfiltration via Cyber Means](https://atlas.mitre.org/techniques/AML.T0025) **MITRE ATLAS**
-- [OWASP Machine Learning Security Top Ten - ML05:2023 Model Theft](https://owasp.org/www-project-machine-learning-security-top-10/docs/ML05_2023-Model_Theft.html) **OWASP ML Top 10**
-- [API4:2023 - Unrestricted Resource Consumption](https://owasp.org/API-Security/editions/2023/en/0xa4-unrestricted-resource-consumption/) **OWASP Web Application Top 10**
-- [OWASP Resource Management](https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/) **OWASP Secure Coding Practices**
\ No newline at end of file
+## LLM10:2025 无限资源消耗
+
+### 描述
+
+无限资源消耗指在大型语言模型（LLM）基于输入查询或提示生成输出的过程中出现的资源滥用现象。推理是LLM的一项关键功能，通过应用已学得的模式和知识生成相关的响应或预测。
+
+攻击者设计的某些攻击旨在中断服务、消耗目标的财务资源，甚至通过克隆模型行为窃取知识产权，这些都依赖于一类共同的安全漏洞才能实现。当LLM应用允许用户进行过多且不受控制的推理时，就会发生无限资源消耗，导致拒绝服务（DoS）、经济损失、模型被窃取及服务降级等风险。LLM的高计算需求，尤其是在云环境中，使其易受资源滥用和未经授权使用的影响。
+
+### 常见漏洞示例
+
+#### 1. 可变长度输入泛滥
+攻击者通过发送大量不同长度的输入，利用处理效率低下的问题。这会消耗大量资源，可能使系统无响应，从而严重影响服务可用性。
+
+#### 2. “钱包拒绝服务”（DoW）
+攻击者通过大量操作利用基于云的AI服务的按使用量收费模式，造成提供方难以承受的财务负担，甚至可能导致财务崩溃。
+
+#### 3. 持续输入溢出
+攻击者持续发送超过LLM上下文窗口限制的输入，导致计算资源过度使用，引发服务降级和运营中断。
+
+#### 4. 资源密集型查询
+提交异常复杂的查询，例如复杂的语句或精细的语言模式，会消耗系统资源，导致处理时间延长甚至系统故障。
+
+#### 5. API模型提取
+攻击者通过精心设计的输入和提示注入技术查询模型API，从而收集足够的输出以复制部分模型或创建影子模型。这不仅会导致知识产权被窃取，还会削弱原模型的完整性。
+
+#### 6. 功能模型复制
+攻击者利用目标模型生成合成训练数据，并用其微调另一基础模型，从而创建功能等价模型。这绕过了传统的基于查询的提取方法，对专有模型和技术构成重大风险。
+
+#### 7. 侧信道攻击
+恶意攻击者可能通过利用LLM的输入过滤技术，执行侧信道攻击以提取模型权重和架构信息。这可能危及模型的安全性并导致进一步的利用。
+
+### 预防和缓解策略
+
+#### 1. 输入验证
+实施严格的输入验证，确保输入不超过合理的大小限制。
+
+#### 2. 限制Logits和Logprobs的暴露
+限制或模糊化API响应中`logit_bias`和`logprobs`的暴露，仅提供必要信息，避免透露详细的概率。
+
+#### 3. 速率限制
+应用速率限制和用户配额，以限制单一来源实体在特定时间内的请求数量。
+
+#### 4. 资源分配管理
+动态监控和管理资源分配，防止单一用户或请求消耗过多资源。
+
+#### 5. 超时与节流
+为资源密集型操作设置超时并限制处理时间，防止资源长时间占用。
+
+#### 6. 沙盒技术
+限制LLM对网络资源、内部服务和API的访问。
+- 这对常见场景尤其重要，因为它涵盖了内部风险和威胁，并控制LLM应用对数据和资源的访问范围，是缓解或防止侧信道攻击的重要控制机制。
+
+#### 7. 全面日志记录、监控和异常检测
+持续监控资源使用情况，并通过日志记录检测和响应异常的资源消耗模式。
+
+#### 8. 水印技术
+实施水印框架，以嵌入和检测LLM输出的未授权使用。
+
+#### 9. 优雅降级
+设计系统在高负载下优雅降级，保持部分功能而非完全故障。
+
+#### 10. 限制队列操作并实现弹性扩展
+限制排队操作的数量和总操作量，同时结合动态扩展和负载均衡，确保系统性能稳定。
+
+#### 11. 对抗性鲁棒性训练
+训练模型检测和缓解对抗性查询及提取企图。
+
+#### 12. 故障令牌过滤
+建立已知故障令牌列表，并在将其添加到模型上下文窗口之前扫描输出。
+
+#### 13. 访问控制
+实施强访问控制，包括基于角色的访问控制（RBAC）和最小权限原则，限制对LLM模型存储库和训练环境的未授权访问。
+
+#### 14. 中央化ML模型清单
+使用中央化的ML模型清单或注册表来管理生产环境中使用的模型，确保适当的治理和访问控制。
+
+#### 15. 自动化MLOps部署
+通过自动化MLOps部署，结合治理、跟踪和审批工作流，加强对基础设施中访问和部署的控制。
+
+### 示例攻击场景
+
+#### 场景 #1: 不受控制的输入大小
+攻击者向处理文本数据的LLM应用提交异常大的输入，导致过多的内存使用和CPU负载，可能使系统崩溃或严重降低服务性能。
+
+#### 场景 #2: 重复请求
+攻击者向LLM API发送大量请求，消耗过多的计算资源，使合法用户无法访问服务。
+
+#### 场景 #3: 资源密集型查询
+攻击者设计特定输入，触发LLM最耗资源的计算过程，导致CPU长期占用，甚至使系统失败。
+
+#### 场景 #4: “钱包拒绝服务”（DoW）
+攻击者生成大量操作，利用基于云的AI服务的按使用量收费模式，造成服务提供商的费用无法承受。
+
+#### 场景 #5: 功能模型复制
+攻击者利用LLM API生成合成训练数据并微调另一模型，从而创建功能等价的模型，绕过传统的模型提取限制。
+
+#### 场景 #6: 绕过系统输入过滤
+恶意攻击者绕过LLM的输入过滤技术和前置规则，执行侧信道攻击，将模型信息提取到远程控制的资源中。
+
+### 参考链接
+
+1. [CVE-2019-20634: Proof of Pudding](https://avidml.org/database/avid-2023-v009/) **AVID**（`moohax` & `monoxgas`）
+2. [arXiv:2403.06634 - 偷窃部分生产语言模型](https://arxiv.org/abs/2403.06634): **arXiv**
+3. [Runaway LLaMA：Meta的LLaMA NLP模型泄露事件](https://www.deeplearning.ai/the-batch/how-metas-llama-nlp-model-leaked/): **Deep Learning Blog**
+4. [我知道你看到的：神经网络侧信道攻击](https://arxiv.org/pdf/1803.05847.pdf): **arXiv 白皮书**
+5. [针对模型提取攻击的全面防御框架](https://ieeexplore.ieee.org/document/10080996): **IEEE**
+6. [Alpaca：强大且可复现的指令跟随模型](https://crfm.stanford.edu/2023/03/13/alpaca.html): **斯坦福大学基础模型研究中心（CRFM）**
+7. [水印如何帮助缓解LLM的潜在风险](https://www.kdnuggets.com/2023/03/watermarking-help-mitigate-potential-risks-llms.html): **KD Nuggets**
+8. [保护AI模型权重以防止窃取和误用](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2800/RRA2849-1/RAND_RRA2849-1.pdf): **RAND Corporation**
+9. [能量-延迟攻击中的海绵示例](https://arxiv.org/abs/2006.03463): **arXiv**
+10. [Sourcegraph API限制漏洞与拒绝服务攻击案例](https://about.sourcegraph.com/blog/security-update-august-2023): **Sourcegraph**
+
+### 相关框架和分类
+
+以下框架和分类提供了关于基础设施部署、环境控制和其他最佳实践的信息、场景和策略：
+
+- [CWE-400: 不受控的资源消耗](https://cwe.mitre.org/data/definitions/400.html): **MITRE Common Weakness Enumeration**
+- [AML.TA0000：机器学习模型访问](https://atlas.mitre.org/tactics/AML.TA0000): **MITRE ATLAS**
+- [AML.T0024：通过ML推理API进行泄露](https://atlas.mitre.org/techniques/AML.T0024): **MITRE ATLAS**
+- [AML.T0029：机器学习服务拒绝](https://atlas.mitre.org/techniques/AML.T0029): **MITRE ATLAS**
+- [AML.T0034：成本滥用](https://atlas.mitre.org/techniques/AML.T0034): **MITRE ATLAS**
+- [AML.T0025：通过网络手段进行泄露](https://atlas.mitre.org/techniques/AML.T0025): **MITRE ATLAS**
+- [OWASP机器学习安全前十 - ML05:2023 模型窃取](https://owasp.org/www-project-machine-learning-security-top-10/docs/ML05_2023-Model_Theft.html): **OWASP ML Top 10**
+- [API4:2023 - 不受控的资源消耗](https://owasp.org/API-Security/editions/2023/en/0xa4-unrestricted-resource-consumption/): **OWASP API安全前十**
+- [OWASP资源管理](https://owasp.org/www-project-secure-coding-practices-quick-reference-guide/): **OWASP 安全编码实践**