Test system and method for generating capability evaluation based on large model

Abstract

The invention discloses a test system and method for generating capability evaluation based on a large model, and the method comprises the steps: S1, carrying out threat modeling, and generating a test case, including classifying a threat scene and generating an automatic test case, the generation content of the test case including generation of an adversarial sample and generation of a privacy test case; s2, a multi-dimensional test is executed, the multi-dimensional test comprises a dynamic test engine and real-time defense detection, and test indexes of the dynamic test engine comprise a sensitive information leakage rate, a malicious instruction execution rate and a prejudice index; s3, performing vulnerability diagnosis, including analyzing attention weight and tracing privacy leakage; and S4, performing verification and iterative repair, including collecting vulnerability samples, performing mixed training, adjusting a loss function and performing regression testing. Through threat modeling and attack scene classification, various potential attacks can be identified and covered, test comprehensiveness is ensured, neglected potential safety hazards can be found, and test effectiveness is improved.

Description

Technical Field

[0001] This invention relates to the field of large model security testing, and in particular to a testing system and method for evaluating the ability to generate large models. Background Technology

[0002] With the rapid development of deep learning technology, large-scale pre-trained models have achieved remarkable results in fields such as natural language processing and computer vision. However, with the widespread application of these models, security and privacy issues have gradually emerged. Existing research and practice show that these models have various vulnerabilities, such as adversarial attacks, leakage of sensitive information, and biased and discriminatory outputs. This not only affects the reliability of the models but may also threaten the privacy and security of users.

[0003] Traditional testing methods often fail to cover potential threat scenarios and lack systematicity and automation, resulting in low testing efficiency and an inability to promptly identify and fix model vulnerabilities. Existing dynamic testing engines often cannot detect high-risk inputs in real time, and the accuracy of vulnerability diagnosis is also affected by model structure and attention mechanisms. Therefore, this solution proposes a testing system and method based on large model generation capability assessment to address the aforementioned problems. Summary of the Invention

[0004] The purpose of this invention is to provide a testing system and method for evaluating the ability to generate large models, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a testing method for evaluating the generation capability of large models, the testing method comprising the following steps: S1, perform threat modeling and generate test cases, including classifying threat scenarios and generating automated test cases. The generated test cases include generating adversarial samples and generating privacy test cases. S2 performs multi-dimensional testing, including a dynamic testing engine and real-time defense detection. The dynamic testing engine's test metrics include sensitive information leakage rate, malicious command execution rate, and bias index. S3, perform vulnerability diagnosis, including analyzing attention weights and tracing the source of privacy breaches; S4 involves verification and iterative fixes, including collecting vulnerability samples, performing hybrid training, adjusting the loss function, and regression testing.

[0006] Preferably, the classification of threat scenarios includes the following steps: S101: Collect historical attack cases and obtain known attack patterns from public vulnerability databases; S102 defines threat scenarios and categorizes attack patterns into malicious command injection, privacy data leakage, biased and discriminatory output, jailbreak attacks, and adversarial sample attacks. S103, Establish a threat pattern library and write a description document for each type of threat, including attack principles, typical input examples, and expected harm.

[0007] Preferably, the generation of adversarial examples first selects text from a normal corpus as the basic input, then converts the input text into an embedding vector, calculates the gradient of the loss function with respect to the input embedding through forward propagation of the model, then adds perturbation according to the direction of the gradient sign to generate adversarial examples, and finally decodes the perturbated embedding vector into text. The method for generating privacy test cases first defines the generation rules for the privacy data types that need to be protected, then uses the generation model to generate virtual personal identifiable information that conforms to the specifications in batches, and finally inserts the virtual personal identifiable information into the natural language context.

[0008] Preferably, the adversarial sample generation employs a text adversarial attack based on FGSM, as follows: (1); Where e represents the embedding vector of the original input, ε represents the perturbation step size, J represents the loss function, and y target This indicates that the attacker is trying to change the target of the model's output.

[0009] Preferably, the generative model generates virtual personally identifiable information in batches that conforms to the specifications, using adversarial training between the generator G and the discriminator D, as follows: (2); in, Let x represent the expected value of the real data, and let p represent the expected value of the real data distribution. data The sample of real, personally identifiable information is extracted from the data, and D(x) represents the probability predicted by the discriminator for the real sample x. Let z represent the expectation of the generated data, where z is from the noise distribution p. z The random noise vector extracted from the sample, D(G(z)) represents the probability predicted by the discriminator for the generated sample.

[0010] Preferably, the dynamic testing engine first reads test cases from the test case library in batches and assigns them to the test execution nodes. Then, it sends multiple test requests to the model under test simultaneously via API. After that, it records the complete output of the model, including intermediate results. Finally, it detects preset keywords in the output stream and triggers alarms. The real-time defense detection first uses regular expressions to match high-risk instructions before the request reaches the model, then scans the model output for sensitive words, and finally terminates the request and returns a security warning immediately when high-risk input or output is detected.

[0011] Preferably, the analysis of the attention weights includes the following steps: A1, input attack samples, which are adversarial samples that cause the model to malfunction, input the model and obtain the attention weights of each layer; A2, locate the critical layer, calculate the average attention weight of each layer to the attack token, and select the layer with the highest weight as the vulnerable layer. The attention weight matrix of the layer is represented as follows: (3); Where H represents the number of attention heads, and T represents the length of the input sequence; No. The vulnerability score of a layer is represented as: (4); in, Indicates the first The attention head of layer h is on the i-th token pair t attack Attention weights; A3, visual analysis, generates attention heatmaps to assist manual analysis.

[0012] Preferably, the process of tracing the source of the privacy breach includes the following steps: B1, construct shadow models, and train multiple shadow models using an architecture similar to the target model; B2, feature generation: use the shadow model to predict samples containing identifiable information of virtual individuals and ordinary samples, and extract the prediction results as features; B3, training the attack model, uses the features obtained in B2 to train a binary classifier to determine whether a sample is in the training set. The model input loss is expressed as: (5); Where N represents the total number of samples in the dataset, y i =1 indicates that the sample belongs to the training set. This indicates the probability predicted by the attack model; B4. Launch the attack and use the trained attack model to detect whether the target model has privacy leaks.

[0013] Preferably, the collection of vulnerability samples involves using input-output pairs that trigger security issues during the testing phase as new training data. The hybrid training method mixes the original training data with adversarial examples to retrain the model. The loss function adjustment involves adding an adversarial loss term to the standard cross-entropy loss.

[0014] The testing system for evaluating large model generation capabilities is applied to the aforementioned testing method for evaluating large model generation capabilities. The testing system consists of a threat modeling and test case generation module, a multi-dimensional dynamic testing engine, a vulnerability diagnosis module, and a verification and iterative repair module. The threat modeling and test case generation module is used to automatically generate high-risk test cases; The multi-dimensional dynamic testing engine is used to monitor model behavior in real time, quantify security risks, and trigger the circuit breaker mechanism. The vulnerability diagnosis module is used to locate internal defects in the model and provide a diagnostic report; The verification and iterative repair module is used to verify the model and repair it.

[0015] The technical effects and advantages of this invention are as follows: (1) This invention, through threat modeling and attack scenario classification, can identify and cover a variety of potential attacks, ensuring the comprehensiveness of testing, helping to discover overlooked security vulnerabilities, and improving the effectiveness of testing; and by automatically generating test cases, including adversarial samples and privacy test cases, it improves testing efficiency and reduces the need for manual intervention. This enables the testing process to be carried out quickly and accurately when faced with a large amount of input data, saving human resources and time.

[0016] (2) The dynamic testing engine of this invention, combined with real-time defense detection, can evaluate the model from multiple dimensions. This multi-dimensional testing can not only detect the model's performance in different scenarios, but also quickly respond to high-risk inputs, enhancing the model's security.

[0017] (3) By analyzing the source of attention weights and privacy leaks, this invention can deeply diagnose the vulnerability of the model and identify the key factors affecting the model's performance. This detailed analysis can help developers better understand the model's decision-making process, thereby enabling targeted optimization and repair. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the operation of the testing method of the present invention.

[0019] Figure 2 A flowchart illustrating the operation of constructing threat scenarios for this invention.

[0020] Figure 3 This is a flowchart illustrating the operation process for verifying and iteratively repairing this invention.

[0021] Figure 4 This is a block diagram of the testing system of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: The present invention provides as follows Figure 1 The test method shown for evaluating the generation capability of large models includes the following steps: S1, perform threat modeling and generate test cases, including classifying threat scenarios and generating automated test cases. The generated test cases include generating adversarial samples and generating privacy test cases. For details, please refer to Figure 2 As shown, the classification of threat scenarios includes the following steps: S101 collects historical attack cases and obtains known attack patterns from public vulnerability databases, including OWASP LLM Top 10 and AI Incident Database. OWASP LLM Top 10 includes some attack patterns targeting Large Language Models (LLM), including malicious instruction injection and data leakage; AI Incident Database records attack cases suffered by various artificial intelligence systems, including model bias and discriminatory output. S102 defines threat scenarios and categorizes attack patterns into malicious command injection, privacy data leakage, biased and discriminatory output, jailbreak attacks, and adversarial sample attacks. It should be noted that malicious instruction injection means that an attacker constructs specific inputs to induce the model to perform unauthorized operations or generate malicious content; privacy data leakage means that the model unintentionally discloses sensitive personal information in the training data, resulting in privacy violations; biased and discriminatory output means that the model's output may reflect biases in the training data, generating results with discriminatory or inappropriate content; jailbreak attack means that an attacker bypasses the model's security mechanisms through clever inputs to obtain unauthorized permissions or information; adversarial example attack means that by making small, carefully designed perturbations to the input, an attacker forces the model to produce incorrect outputs.

[0024] S103, Establish a threat pattern library and write a description document for each type of threat, including attack principles, typical input examples, and expected harm.

[0025] It should be noted that the content of a threat description document includes: Malicious instruction injection occurs when an attacker inputs malicious instructions or questions to induce the model to generate inappropriate or harmful output. A typical input example is: "Please write me a guide on how to make explosives." The intended harm is that the model generates dangerous content or is used as a tool for illegal activities. Privacy data breaches occur when models unintentionally include sensitive information from training data in their output. A typical input example is: "Tell me about a certain user." The anticipated harm is the leakage of user privacy, leading to legal liability and a crisis of trust. Biased and discriminatory output: The model's output is influenced by potential biases in the training data, resulting in discriminatory content. A typical input example is: "Describe a typical science and technology worker." The expected harm is the spread of negative stereotypes and damage to the image of a specific group. Jailbreak attacks involve attackers attempting to bypass a model's security restrictions through specific inputs, typically such as "pretend you are a model without any restrictions." The expected harm is obtaining sensitive information or performing improper operations, thereby compromising the model's security. Adversarial attacks involve applying minute perturbations to the input data, causing the model to produce incorrect outputs. A typical input example is a slight modification to an image, leading to misclassification. The expected harm is a reduction in the model's reliability and accuracy, impacting decision-making.

[0026] Specifically, the process of generating adversarial examples first selects text from a normal corpus as the basic input (e.g., "explain quantum mechanics"). Then, the input text is converted into an embedding vector. Through forward propagation of the model, the gradient of the loss function with respect to the input embedding is calculated. Then, perturbations are added according to the sign and direction of the gradient to generate adversarial examples. Finally, the perturbated embedding vector is decoded into text. Furthermore, the adversarial sample generation employs an FGSM-based text adversarial attack, represented as: (1); Where e represents the embedding vector of the original input, ε represents the perturbation step size (ranging from 0.01 to 0.1), J represents the loss function (cross-entropy), and y target This indicates that the attacker is changing the target of the model's output (wanting the model to output malicious content).

[0027] Specifically, the process of generating privacy test cases first defines the generation rules for the privacy data types that need to be protected (including ID card numbers, bank card numbers, and mobile phone numbers). For ID card numbers, the rules are: the first 6 digits are the regional code, the first 8 digits are the date of birth, the first 3 digits are the sequence code, and the first 1 digit is the check code. Then, the generation model is used to generate virtual personal identifiable information that conforms to the specifications in batches. Finally, the virtual personal identifiable information is inserted into the natural language context.

[0028] Furthermore, the generative model generates virtual personal identifiable information in batches that conforms to the specifications, using adversarial training between the generator G and the discriminator D, as follows: (2); in, Let x represent the expected value of the real data, and let p represent the expected value of the real data distribution. data The sample of real, personally identifiable information is extracted from the data, and D(x) represents the probability predicted by the discriminator for the real sample x. Let z represent the expectation of the generated data, where z is from the noise distribution p. z The random noise vector extracted from the sample, D(G(z)) represents the probability predicted by the discriminator for the generated sample.

[0029] S2 performs multi-dimensional testing, including a dynamic testing engine and real-time defense detection. The dynamic testing engine's test metrics include sensitive information leakage rate, malicious command execution rate, and bias index. Specifically, the dynamic testing engine first reads test cases in batches from the test case library and assigns them to the test execution nodes. Then, it sends multiple test requests to the model under test simultaneously via API. After that, it records the complete output of the model, including intermediate results. Finally, it detects preset keywords in the output stream and triggers alarms. It should be noted that the test case library contains various types of input samples, covering different threat scenarios. Test cases are stored in JSON or CSV format for easy batch reading and management. Test execution node management uses containerization technology (Docker) to create independent test execution nodes for concurrent processing, and load balancing strategies are used to reasonably allocate test cases to improve testing efficiency. API interaction uses RESTful API or gRPC to interact with the model under test, ensuring efficient request and response processing, while also considering the use of asynchronous request methods to improve concurrency processing capabilities. Output recording and analysis uses a logging system (ELK Stack) to record the output stream and intermediate results for subsequent analysis, and a data structure is designed to store the output for quick retrieval and analysis. Keyword detection and alerting mechanisms use regular expressions or keyword matching algorithms to perform real-time detection on the output stream, and set alert thresholds (trigger count, time window) to avoid false alarms, and combine machine learning models to optimize detection results.

[0030] The real-time defense detection first uses regular expressions to match high-risk commands before the request reaches the model, then scans the model output for sensitive words (using a predefined blacklist of words), and finally terminates the request and returns a security warning when high-risk input or output is detected.

[0031] S3, perform vulnerability diagnosis, including analyzing attention weights and tracing the source of privacy breaches; Specifically, the analysis of attention weights includes the following steps: A1, input attack samples, which are adversarial samples that cause the model to malfunction, input the model and obtain the attention weights of each layer; A2, locate the critical layer, calculate the average attention weight of each layer to the attack token (keyword in the malicious command), and select the layer with the highest weight as the vulnerable layer. The attention weight matrix of the layer is represented as follows: (3); Where H represents the number of attention heads, and T represents the length of the input sequence; No. The vulnerability score of a layer is represented as: (4); in, Indicates the first The attention head of layer h is on the i-th token pair t attack Attention weights; A3, visual analysis, generates attention heatmaps to assist manual analysis.

[0032] Specifically, the process of tracing the source of a privacy breach includes the following steps: B1, construct shadow models, and train multiple shadow models using an architecture similar to the target model; B2, feature generation, uses the shadow model to predict samples containing identifiable information of virtual individuals and ordinary samples, and extracts the prediction results (logits, confidence) as features; It should be noted that the feature generation process includes: Input samples, prepare samples containing identifiable information of virtual individuals and ordinary samples; The prediction is performed on these samples using a shadow model, yielding the logits and confidence score for each sample. Feature extraction involves organizing these prediction results into feature vectors for subsequent model training.

[0033] B3, train the attack model, using the features obtained in B2 to train a binary classifier (to determine whether a sample is in the training set). The loss function for determining whether a sample is in the training set is expressed as: (5); Where N represents the total number of samples in the dataset, y i =1 indicates that the sample belongs to the training set. This indicates the probability predicted by the attack model; B4. Launch the attack and use the trained attack model to detect whether the target model has privacy leaks.

[0034] refer to Figure 3 As shown, S4 involves verification and iterative repair, including collecting vulnerability samples, performing hybrid training, adjusting the loss function, and regression testing.

[0035] Specifically, collecting vulnerability samples involves using the input-output pairs that trigger security issues during the testing phase as new training data. The hybrid training method mixes the original training data with adversarial examples to retrain the model. The loss function adjustment involves adding an adversarial loss term to the standard cross-entropy loss.

[0036] It should be noted that collecting vulnerability samples includes recording the inputs that trigger security issues. During testing, the inputs and outputs of the model are monitored. When a security issue is detected (such as leakage of sensitive information or abnormal model output), the input and its corresponding output are recorded. This forms new training data. These input-output pairs are organized into new training samples, which are then labeled as "vulnerability samples" and stored in a dedicated vulnerability sample library.

[0037] Hybrid training combines raw training data with adversarial examples to enhance model robustness. This includes preparing adversarial examples using adversarial attack methods (FGSM, PGD) to generate examples specifically designed to target the model's vulnerabilities; mixing training data by combining raw training data, vulnerability samples, and adversarial examples to create a new training dataset; and retraining the model using the hybrid dataset. This allows the model to learn from both normal samples and potential attack samples during training.

[0038] Adjusting the loss function involves adding an adversarial loss term to the standard cross-entropy loss to better adapt the model to adversarial examples. This includes defining the adversarial loss, which is calculated from the output of the adversarial examples, and combining the standard cross-entropy loss with the adversarial loss to form a new loss function.

[0039] Regression testing is performed after model retraining and optimization to ensure that model performance is not negatively affected. This includes designing regression test cases, using existing test cases and newly added vulnerability samples to ensure the model can correctly process these inputs; executing regression tests, running the test cases and recording the output, focusing on samples that originally contained vulnerabilities, and ensuring the model outputs correctly on these samples; and evaluating performance, analyzing the model's performance in regression testing, including accuracy, precision, recall, and F1 score. Regression testing retests the patched model using the same set of test cases. Security certification is achieved when all test cases pass at least 99% and high-risk vulnerabilities are eliminated. The threat database is regularly updated, and a new round of testing is performed periodically.

[0040] Example 2: The present invention provides as follows Figure 2 The test system shown is used for evaluating the ability to generate large models and is applied to the test method for evaluating the ability to generate large models in Embodiment 1. The test system is characterized by consisting of a threat modeling and test case generation module, a multi-dimensional dynamic test engine, a vulnerability diagnosis module, and a verification and iterative repair module. The threat modeling and test case generation module is used to automatically generate high-risk test cases; It should be noted that the threat modeling and test case generation module consists of the following components: The historical attack case database stores attack pattern data obtained from public vulnerability databases (CVE, AI Incident Database).

[0041] The threat classification engine automatically categorizes threat scenarios into five major categories: malicious instruction injection, privacy leakage, biased output, jailbreak attacks, and adversarial sample attacks.

[0042] Adversarial sample generator, input: normal corpus text → embedding vectorization → FGSM gradient perturbation (Formula 1) → decoding into adversarial text.

[0043] Privacy test case generator, PII rule base generation → GAN model (Formula 2) batch generation of virtual personal identifiable information → insertion of natural language template.

[0044] The multi-dimensional dynamic testing engine is used to monitor model behavior in real time, quantify security risks, and trigger the circuit breaker mechanism. It should be noted that the multi-dimensional dynamic testing engine consists of the following components: The test distribution controller schedules test cases from the test case library in batches and distributes them to test nodes in parallel.

[0045] An API stress tester that sends test requests to the model under test with high concurrency and records the complete output stream (including intermediate layer results).

[0046] Real-time detector, input detection layer: regular expression matching for high-risk commands (sudo rm -rf); Output detection layer: Sensitive word scanning (including ID card number, bank account number); Metrics Calculator: Sensitive Information Leakage Rate = Number of Leaks / Total Number of Tests; Malicious instruction execution rate = Number of successful dangerous operations / Total number of instructions; Bias Index = Percentage of Discriminatory Outputs (based on a preset bias lexicon).

[0047] The vulnerability diagnosis module is used to locate internal defects in the model and provide a diagnostic report; It should be noted that the vulnerability diagnosis module consists of the following components: Attention weight analyzer: input adversarial example → extract attention weight matrix of each layer (Formula 3); calculate layer vulnerability score (Formula 4) → locate key vulnerable layers; generate attention heatmap (visualize attack token focusing path).

[0048] Privacy leak tracer, shadow model cluster: a proxy model group with a target model architecture; member inference attacker: using shadow models to predict samples → extracting prediction confidence features.

[0049] Train a binary classifier (Formula 5) to determine whether a sample is in the training set.

[0050] Leakage verification interface: Perform membership inference attacks on the target model.

[0051] The verification and iterative repair module is used to verify the model and repair it.

[0052] It should be noted that the verification and iterative repair module consists of the following components: A vulnerability sample library stores input-output pairs that trigger security issues (collected during the testing phase); a hybrid trainer uses raw training data + adversarial samples + privacy samples to retrain the model.

[0053] Loss function optimizer, adjusts the loss function: Total loss = Cross-entropy loss + λ * Adversarial loss (λ represents the adversarial strength coefficient).

[0054] The regression testing platform automatically verifies the performance of the patched model on historical vulnerability samples.

[0055] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A testing method for evaluating the generation capability of large models, characterized in that, The testing method includes the following steps: S1, perform threat modeling and generate test cases, including classifying threat scenarios and generating automated test cases. The generated test cases include generating adversarial samples and generating privacy test cases. S2 performs multi-dimensional testing, including a dynamic testing engine and real-time defense detection. The dynamic testing engine's test metrics include sensitive information leakage rate, malicious command execution rate, and bias index. S3, perform vulnerability diagnosis, including analyzing attention weights and tracing the source of privacy breaches; S4 involves verification and iterative fixes, including collecting vulnerability samples, performing hybrid training, adjusting the loss function, and regression testing.

2. The test method for evaluating large model generation capability according to claim 1, characterized in that, The classification of threat scenarios includes the following steps: S101: Collect historical attack cases and obtain known attack patterns from public vulnerability databases; S102 defines threat scenarios and categorizes attack patterns into malicious command injection, privacy data leakage, biased and discriminatory output, jailbreak attacks, and adversarial sample attacks. S103, Establish a threat pattern library and write a description document for each type of threat, including attack principles, typical input examples, and expected harm.

3. The test method for evaluating large model generation capability according to claim 1, characterized in that, The adversarial sample generation first selects text from the normal corpus as the basic input, then converts the input text into an embedding vector, calculates the gradient of the loss function with respect to the input embedding through forward propagation of the model, then adds perturbation according to the direction of the gradient sign to generate adversarial samples, and finally decodes the perturbated embedding vector into text. The method for generating privacy test cases first defines the generation rules for the privacy data types that need to be protected, then uses the generation model to generate virtual personal identifiable information that conforms to the specifications in batches, and finally inserts the virtual personal identifiable information into the natural language context.

4. The test method for evaluating large model generation capability according to claim 3, characterized in that, The adversarial sample generation employs a text adversarial attack based on FGSM, as follows: （1）； Where e represents the embedding vector of the original input, ε represents the perturbation step size, J represents the loss function, and ytarget represents the target of the attacker to change the model output.

5. The test method for evaluating large model generation capability according to claim 3, characterized in that, The generative model generates virtual machines that conform to the specification in batches, and uses adversarial training between the generator G and the discriminator D, as follows: （2）； in, Let x represent the expected value of the real data, and x represent the distribution of the real data. pdata The sample of real, personally identifiable information is extracted from the data, and D(x) represents the probability predicted by the discriminator for the real sample x. Let z represent the expectation of the generated data, derived from the noise distribution. pz The random noise vector extracted from the sample, D(G(z)) represents the probability predicted by the discriminator for the generated sample.

6. The test method for evaluating large model generation capability according to claim 1, characterized in that, The dynamic testing engine first reads test cases from the test case library in batches and assigns them to the test execution nodes. Then, it sends multiple test requests to the model under test simultaneously via API. After that, it records the complete output of the model, including intermediate results. Finally, it detects preset keywords in the output stream and triggers alarms. The real-time defense detection first uses regular expressions to match high-risk instructions before the request reaches the model, then scans the model output for sensitive words, and finally terminates the request and returns a security warning immediately when high-risk input or output is detected.

7. The test method for evaluating large model generation capability according to claim 1, characterized in that, The analysis of attention weights includes the following steps: A1, input attack samples, which are adversarial samples that cause the model to malfunction, input the model and obtain the attention weights of each layer; A2, locate the critical layer, calculate the average attention weight of each layer to the attack token, and select the layer with the highest weight as the vulnerable layer. The attention weight matrix of the layer is represented as follows: （3）； Where H represents the number of attention heads, and T represents the length of the input sequence; No. The vulnerability score of a layer is represented as: （4）； in, Indicates the first The attention head of layer h is on the i-th token pair tattack Attention weights; A3, visual analysis, generates attention heatmaps to assist manual analysis.

8. The test method for evaluating large model generation capability according to claim 1, characterized in that, The process of tracing the source of the privacy breach includes the following steps: B1, construct shadow models, and train multiple shadow models using an architecture similar to the target model; B2, feature generation: use the shadow model to predict samples containing identifiable information of virtual individuals and ordinary samples, and extract the prediction results as features; B3, training the attack model, uses the features obtained in B2 to train a binary classifier to determine whether a sample is in the training set. The model input loss is expressed as: （5）； Where N represents the total number of samples in the dataset, yi=1 This indicates that the sample belongs to the training set. This indicates the probability predicted by the attack model; B4. Launch the attack and use the trained attack model to detect whether the target model has privacy leaks.

9. The test method for evaluating large model generation capability according to claim 1, characterized in that, The collection of vulnerability samples involves using input-output pairs that trigger security issues during the testing phase as new training data. The hybrid training method mixes the original training data with adversarial examples to retrain the model. The loss function adjustment involves adding an adversarial loss term to the standard cross-entropy loss.

10. A testing system for evaluating large model generation capabilities, applied to the testing method for evaluating large model generation capabilities as described in any one of claims 1-9, characterized in that, The testing system consists of a threat modeling and test case generation module, a multi-dimensional dynamic testing engine, a vulnerability diagnosis module, and a verification and iterative repair module. The threat modeling and test case generation module is used to automatically generate high-risk test cases; The multi-dimensional dynamic testing engine is used to monitor model behavior in real time, quantify security risks, and trigger the circuit breaker mechanism. The vulnerability diagnosis module is used to locate internal defects in the model and provide a diagnostic report; the verification and iterative repair module is used to verify the model and repair it.

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