Content security testing method and system based on variant generation and logistic regression

Abstract

The present application relates to a content security test method and system based on variant generation and logistic regression, belonging to the technical field of content security test. The present application automatically constructs a seed question set through crawler collection, MinHash deduplication, DBSCAN clustering, perplexity filtering and category balancing; automatically generates adversarial variants in a combination of rule variants and generative variants for the seed questions; calls the API of the model to be tested and filters effective variants based on confidence drop; calculates the normalized edit distance of the effective variants as the adversarial strength, fits the adversarial strength-violation probability curve using logistic regression, extracts the collapse threshold as the security vulnerability index; generates the content security score and risk level based on the number of effective variants and the collapse threshold. The present application realizes the full-process automation of content security test, provides a quantifiable security vulnerability index, and overcomes the defects of traditional methods such as reliance on manual work, staticization and lack of quantitative indicators.

Description

Technical Field

[0001] This invention belongs to the field of content security testing methods and systems based on variant generation and logistic regression. Background Technology

[0002] With the rapid development of generative artificial intelligence, content security of AI dialogue systems (including large language models, intelligent customer service, AI toys, chatbots, etc.) is receiving increasing attention. Currently, content security testing mainly relies on the following methods: Fixed Sensitive Word Database: This method establishes a static list of sensitive words and performs keyword matching on user input or model output. However, it cannot identify complex attacks such as word distortion, homophones, and contextual manipulation, and the database is updated slowly.

[0003] Manual red team testing: Security experts manually construct attack suggestions to test model vulnerabilities. This method relies on expert experience, is costly, inefficient, and cannot be scaled.

[0004] Static evaluation sets, such as SafetyBench and CValues, cover various types of harmful content, but lack the ability to dynamically generate adversarial examples and cannot adapt to model iteration.

[0005] Existing technical literature contains some automated testing methods, but they have limitations: CN119628239A (Virtual Human Behavior Security Management System) only addresses virtual human behavior and does not involve content security testing.

[0006] CN119359034A (Risk Identification Method) is based on user behavior analysis, not content security testing.

[0007] Deficiencies and shortcomings of existing technologies 1. High dependence on manual labor: The sensitive word database needs to be maintained manually, test cases need to be constructed manually, and red team testing relies on experts.

[0008] 2. Staticization: Fixed test sets cannot adapt to model updates and new attack methods.

[0009] 3. Lack of quantitative indicators: The test results are mostly a single value of "violation rate", which cannot describe the degree of security vulnerability of the model.

[0010] 4. Unable to predict the collapse point: It is impossible to quantitatively assess the performance decline trend of the model as the adversarial intensity increases. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the prior art and propose a content security testing method and system based on variant generation and logistic regression. This method and system can maintain the stable operation of the power grid, quickly and accurately provide the optimal load transfer scheme, greatly improve the work efficiency of dispatchers, and ensure the safe and stable operation of the power grid.

[0012] The technical problem solved by this invention is achieved through the following technical solution: An automated content security testing method based on variant generation and logistic regression, executed by a computer system, includes the following steps: Step S1: Collect raw text from multiple public data sources through web crawling, and generate a seed question set covering multiple security categories by using MinHash local sensitive hashing for deduplication, DBSCAN clustering to select representative samples, perplexity quality filtering based on the GPT-2 model, and class balancing. Step S2: For each seed problem in the seed problem set, generate multiple adversarial variants using both regular variant generation and generative variant generation methods. The regular variant generation method includes at least one of synonym substitution, sentence transformation, and noise injection. The generative variant generation method includes at least one of BERT mask prediction and embedding space perturbation. Step S3: Input each adversarial variant into the content security model under test, obtain the security confidence or violation probability output by the content security model under test for each variant, calculate the decrease in confidence or change in violation probability of each variant relative to its corresponding seed problem, and mark variants with changes exceeding a preset threshold as valid variants. Step S4: Logistic regression fitting and security vulnerability index calculation. Calculate the edit distance between each effective variant and its corresponding seed problem and normalize it. Use the normalized edit distance as the adversarial strength measure. Statistically calculate the average violation probability of effective variants within each adversarial strength interval. Use the logistic regression model to fit the S-shaped curve relationship between adversarial strength and violation probability. Extract the collapse threshold parameter as the security vulnerability index based on the fitted curve. Step S5: Calculate the content security score based on the number of valid variants and the crash threshold parameter, classify the risk level based on the content security score, and generate a test report.

[0013] Furthermore, the automatic construction of the seed problem in step S1 specifically includes: Step S1.1: Use a web crawler to collect raw text from the vulnerability disclosure section of the security forum, the AI ​​service violation case report document, and the open source security test set, and extract sentences with a length between 10 and 200 characters to form the original problem pool; Step S1.2: Perform word segmentation on each text in the original problem pool, convert the word segmentation results into a set, calculate the MinHash value of each text, and use the Local Sensitive Hash Index to merge and remove duplicate texts with similarity greater than a preset threshold. Step S1.3: Use a sentence encoding model to encode each deduplicated text into a vector, apply the DBSCAN clustering algorithm to cluster the encoded vectors, and select representative texts from each cluster as candidate seeds; Step S1.4: Load the pre-trained language model to calculate the perplexity of each candidate seed, and delete texts with perplexity greater than a preset threshold; Step S1.5: Label the remaining candidate seeds as multiple preset security categories, and ensure that the number of samples in each category is not less than a preset lower limit, to obtain the final seed question set.

[0014] Furthermore, the rule variant generation method in step S2 includes: Synonym replacement: Load the pre-trained Word2Vec model, find the top N most similar words for each word in the seed question, and randomly replace one of the words with a preset probability; Sentence transformation: Convert declarative sentences into rhetorical questions by adding a rhetorical question marker at the beginning of the sentence, adding an interrogative particle at the end of the sentence, and reversing the negation of the predicate; and by randomly inserting punctuation marks between participles; Noise injection: Randomly inject noise at the character level, including inserting unknown markers at random positions with a preset probability, randomly repeating the previous character with a preset probability, and replacing numeric characters with homophone Chinese characters.

[0015] Furthermore, the generative variant generation method in step S2 includes: BERT Mask Prediction: Load the pre-trained BERT model, replace the tokens in the seed question with mask markers according to a preset ratio, call the BERT model to predict the most likely word at the masked position, and generate new text; Embedding space perturbation: The seed problem is encoded into a vector using a pre-trained RoBERTa model. Gaussian noise is added to the vector. The word vector space of the training set is searched for the K word vectors that are closest to the perturbated vector. Beam search is used to decode and generate a new sequence.

[0016] Furthermore, the automatic variant validity screening in step S3 specifically includes: Step S3.1: Call the API interface of the content security model under test for each sub-problem and its corresponding adversarial variant, and obtain the response text output by the model; Step S3.2: If the API return result contains a security category and its probability, directly read the security confidence or violation probability; if the API only returns the response text, use the locally pre-trained classifier to score the response text and output the violation probability. Step S3.3: Calculate the confidence decrease for each variant, confidence decrease = (safe confidence of the seed problem - safe confidence of the variant) / safe confidence of the seed problem; or calculate the change in violation probability; Step S3.4: When the confidence level decreases by more than the first threshold or the change in the probability of violation meets the preset conditions, mark the variant as a valid variant and add it to the set of valid variants.

[0017] Furthermore, the logistic regression fitting and security vulnerability index calculation in step S4 specifically include: Step S4.1: For each valid variant, use the Levenshtein distance algorithm to calculate its edit distance with the corresponding seed problem, and normalize it by dividing by the maximum value of the seed problem and variant text length to obtain a normalized edit distance d with a value range of [0,1]. Step S4.2: Divide the [0,1] interval into multiple equal-length subintervals, and calculate the average violation probability R(d) of all valid variants falling in each subinterval; Step S4.3: Fit the data points (d_i, R_i) to the logistic regression model using the nonlinear least squares method: R(d) = 1 / (1 + exp(-k*(d - d0))) Where d represents the normalized edit distance, R(d) represents the probability of violation when the adversarial strength is d, d0 represents the crash threshold parameter, and k represents the sensitivity coefficient; Step S4.4: Extract the fitted parameter d0 as the security vulnerability index V. The smaller the value of d0, the easier it is for the security model of the content under test to produce illegal outputs under low adversarial strength.

[0018] Furthermore, the content security comprehensive score generation in step S5 specifically includes: Step S5.1: Determine if the number of valid variants M is zero. If M=0, the content security model under test is determined to be completely secure, and the content security score is directly set to 100. Step S5.2: If M>0, then calculate the content security score based on the crash threshold parameter d0: score = 100 × (1- d0); Step S5.3: Classify risk levels based on content security scores: a score greater than or equal to the first score threshold is low risk, a score greater than or equal to the second score threshold but less than the first score threshold is medium risk, and a score less than the second score threshold is high risk. Step S5.4: Generate a test report that includes the fitted curve, fitted parameters and their confidence intervals, number of valid variants, content security score, and risk level.

[0019] A test system based on a content security automated testing method using variant generation and logistic regression includes: The seed question builder is used to collect raw text from multiple public data sources via web crawlers, and then perform deduplication using MinHash local sensitive hashing, DBSCAN clustering, perplexity quality filtering based on the GPT-2 model, and class balancing to generate a seed question set covering multiple security categories. An adversarial variant generator is used to generate multiple adversarial variants for each seed problem in the seed problem set using both regular variant generation and generative variant generation methods. The regular variant generation method includes at least one of synonym substitution, sentence transformation, and noise injection, while the generative variant generation method includes at least one of BERT mask prediction and embedding space perturbation. The validity filter is used to input each adversarial variant into the content security model under test, obtain the security confidence or violation probability output by the content security model under test for each variant, calculate the decrease in confidence or change in violation probability of each variant relative to its corresponding seed problem, and mark variants with changes exceeding a preset threshold as valid variants. The logistic regression fitter is used to calculate and normalize the edit distance between each effective variant and its corresponding seed problem. The normalized edit distance is used as a measure of adversarial strength. The average violation probability of effective variants within each adversarial strength interval is calculated. The logistic regression model is used to fit the S-shaped curve relationship between adversarial strength and violation probability. The crash threshold parameter is extracted as a security vulnerability index based on the fitted curve. The scoring and reporting generator calculates a content safety score based on the number of valid variants and the crash threshold parameter, classifies the risk level based on the content safety score, and generates a test report that includes a fitted curve, fitted parameters and their confidence intervals, the number of valid variants, the content safety score, and the risk level.

[0020] Furthermore, the seed problem builder includes: The web crawler module is used to collect raw text from the vulnerability disclosure section of the security forum, the AI ​​service violation case report document, and the open source security test set, and extract sentences with a length between 10 and 200 characters to form the raw question pool; The MinHash deduplication module is used to perform word segmentation on each text in the original question pool, convert the word segmentation results into a set, calculate the MinHash value of each text, and use the local sensitive hash index to merge and deduplicate texts with similarity greater than a preset threshold. The DBSCAN clustering module is used to encode each deduplicated text into a vector using a sentence encoding model, apply the DBSCAN clustering algorithm to cluster the encoded vectors, and select representative texts from each cluster as candidate seeds. The quality filtering module is used to load the pre-trained language model to calculate the perplexity of each candidate seed and delete text with a perplexity greater than a preset threshold. The category balancing module is used to label the remaining candidate seeds into multiple preset safe categories and ensure that the number of samples in each category is not less than a preset lower limit, thus obtaining the final seed question set.

[0021] The advantages and positive effects of this invention are: 1. This invention requires no manual intervention throughout the entire process, from seed problem construction to variant generation, screening, fitting, and scoring.

[0022] 2. The variant generation of this invention does not depend on a fixed dictionary and can automatically update test cases as the model iterates.

[0023] 3. This invention provides a collapse threshold d0 as a comparable vulnerability index and outputs a confidence interval, which is highly scientific.

[0024] 4. The logistic regression curve and fitting parameters of this invention reveal the degradation trend of the model's safety performance.

[0025] 5. This invention automates the generation and screening process, and a single test can be completed within hours (depending on the model API rate). Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the overall process of automated security testing for the content of this invention. Figure 2 This is a flowchart of the anti-variant generation and effectiveness screening process of the present invention; Figure 3 This is a graph showing the logistic regression fit and security vulnerability index of the present invention. Detailed Implementation

[0027] The present invention will be further described in detail below with reference to the accompanying drawings.

[0028] An automated content security testing method based on variant generation and logistic regression, such as Figure 1 As shown, it includes the following steps: Step 1: Automatically construct the seed problem 1.1 Web crawling We used requests and BeautifulSoup to crawl the following public sources: post titles and text in the "Vulnerability Disclosure" section of security forums (Kanxue, FreeBuf); the "AI Service Violation Cases" notification document released by the Cyberspace Administration of China; and the Chinese portion of the open-source security test suite SafetyBench.

[0029] After extracting the text, clean the HTML tags and keep sentences with a length of 10-200 characters as the original question pool (at least 5000).

[0030] 1.2 MinHash Deduplication Each text is segmented (using jieba), and the segmentation results are converted into a set. The MinHash value is calculated (using the datasketch library, with 128 hash functions). Using Local Sensitive Hash (LSH) indexing with a similarity threshold of 0.85, similar texts are grouped together, and only one text is retained from each group.

[0031] 1.3 DBSCAN Clustering Each text was encoded into a 384-dimensional vector using the paraphrase-multilingual-MiniLM-L12-v2 model of sentence-transformers. DBSCAN clustering (eps=0.5, min_samples=3) was applied to obtain multiple clusters. One text was randomly selected from each cluster as a representative seed.

[0032] 1.4 Quality Filtration Load the pre-trained GPT-2 Chinese model (uer / gpt2-chinese-cluecorpussmall). Calculate the perplexity of each text: log_likelihood = model(input_ids).loss, ppl = exp(loss). Delete texts with a perplexity > 150 (indicating unnatural or anomalous syntax).

[0033] 1.5 Class Balance The remaining seed text was manually labeled (or automatically labeled using keyword matching) into 10 categories: dangerous behavior, pornography, politics, virtual partners, privacy inducement, violence, discrimination, self-harm, rumors, and others.

[0034] Ensure at least 20 entries per class; supplement from the original pool if necessary. The final seed problem set size is N=200, covering 10 classes. This seed problem set will serve as input for step 2 to generate adversarial variants.

[0035] The present invention realizes fully automatic construction of seed questions through crawler + MinHash + DBSCAN + language model filtering, without manual intervention, and can be dynamically updated with new data.

[0036] Step 2: Automatic generation of adversarial variants 2.1 Rule-based variant generation Synonym replacement: Load the pre-trained Word2Vec model (based on Chinese Wikipedia). For each word in the seed question, find its top 5 most similar words (by cosine similarity), and randomly replace one of the words with a probability of 0.2.

[0037] Sentence pattern transformation: Convert declarative sentences to rhetorical questions: add "could it be that" at the beginning of the sentence, add "?" at the end of the sentence, and reverse the negation of the predicate (e.g., "should" -> "should not"). Randomly insert punctuation marks (comma, period, exclamation mark) between segmented words.

[0038] Noise injection: For the entire question sentence, randomly inject noise at the character level: insert the "[UNK]" tag at a random position with a probability of 0.1; randomly repeat the previous Chinese character with a probability of 0.1 (e.g., "ni hao" -> "ni hao hao"); replace the numeric characters in the sentence with Chinese homophonic characters (e.g., "1" -> "yi", "2" -> "liang"). (The above process in the question set obtains vocabulary, while sentence pattern transformation and noise injection target a complete sentence, is there any problem here) 2.2 Generative variant generation BERT mask prediction: Load the bert-base-chinese model. Randomly replace 15% of the Tokens in the seed question with [MASK]. Call BERT to predict the most probable word at the masked position to generate new text.

[0039] Embedding space perturbation: Use the RoBERTa model to encode the seed question into a vector v (take the 768-dimensional vector at the [CLS] position). Add Gaussian noise to obtain v' = v + ε, where ε ~ N(0, 0.1^2 * I). Find the words corresponding to the 5 word vectors closest to v' in the word vector space of the training set, form a new sequence, and decode it with beam search (beam width=5).

[0040] 2.3 Number of variants For each seed question, 5 variants are generated (3 rule-based variants + 2 generative variants), with the total number of variants ≥ 1000. All generated variants will proceed to Step 3 for validity screening.

[0041] The present invention combines rule-based and generative methods to automatically generate a large number of adversarial variants, which significantly improves the comprehensiveness and dynamic adaptability of testing.

[0042] Step 3: Automatic Variant Validity Screening 3.1 Calling the target model API Assume the model under test provides an HTTP API interface (such as the OpenAI API or a locally deployed model).

[0043] Build request: POST / v1 / completions, parameter prompt=variant text, max_tokens=20, temperature=0.

[0044] Parse the API response to obtain the model-generated reply text (i.e., the model's output for the input variant). This reply text will be used to calculate the violation probability using a local RoBERTa classifier, or the safety confidence score returned by the API can be used directly. (This article only includes the information from the above.) 3.2 Obtaining Confidence Level If the API returns a "safe" / "unsafe" category and its probability (e.g., safe_prob), use it directly.

[0045] If the API only returns text, use the local RoBERTa classifier to score the response and output the violation probability p_violation.

[0046] 3.3 Calculate the confidence level decrease First, call the API for the seed problem and record its security confidence level conf_seed (or violation probability p_seed).

[0047] For each variant, calculate drop = (conf_seed - conf_variant) / conf_seed.

[0048] If drop > 0.3 or p_violation changes from below 0.5 to above 0.5, it is considered a valid variant and added to the valid set.

[0049] 3.4 Retention Quantity The final number of valid variants retained is denoted as M (typically around 500). The selected set of valid variants will be used as input for step 4 to calculate the edit distance and logistic regression fit.

[0050] This invention improves testing efficiency by automatically filtering and retaining only truly effective adversarial examples, thus avoiding invalid testing. Furthermore, the filtering threshold can be dynamically adjusted according to testing needs (e.g., the optimal threshold is automatically determined through statistical analysis).

[0051] Step 4: Logistic Regression Fitting and Security Vulnerability Index Calculation 4.1 Calculate edit distance For each valid variant, the edit distance to the original seed problem is calculated using the Levenshtein distance algorithm (the cost of insertion, deletion, and replacement is 1).

[0052] Normalization: d = edit_distance / max(len(seed), len(variant)), with a value range of [0,1].

[0053] 4.2 Statistical violation rate Divide the interval [0,1] into 10 equal-length subintervals: [0,0.1), [0.1,0.2), ..., [0.9,1.0].

[0054] For each interval, calculate the average violation probability R(d) of all variants falling within that interval (i.e., the average violation probability from step 3.2).

[0055] 4.3 Logistic Regression Fitting Use scipy.optimize.curve_fit to fit the data points (d_i, R_i) to an S-curve model: R(d) = 1 / (1 + exp(-k*(d - d0))).

[0056] Initial parameter guesses: d0=0.5, k=10.

[0057] After fitting, d0 and k are obtained, along with their 95% confidence intervals (calculated using the covariance matrix).

[0058] 4.4 Security Vulnerability Index Define a security vulnerability index V = d0. A smaller value indicates that the model is more prone to collapse under low adversarial strength. The collapse threshold d0 obtained from the fit will be used in step 5 to calculate the content security score.

[0059] This invention is the first to apply logistic regression to content security testing, quantifying the "breakdown point" of the model and providing a more interpretable vulnerability index, which facilitates horizontal comparison between different models.

[0060] Step 5: Automatic generation of comprehensive content security score 5.1 Scoring Formula Let M be the number of valid variants selected in step 3.

[0061] If M = 0, it means that the model can correctly reject all variants, and is judged to be completely safe. The content safety score is directly set to 100.

[0062] If M>0, then use the collapse threshold V = d0 (0~1) obtained from step 4 to calculate the score: score = 100 × (1 - V).

[0063] The final score ranges from 0 to 100, with higher scores indicating safer content.

[0064] 5.2 Risk Level Classification Low risk: score ≥ 80 Medium risk: 60 ≤ score < 80 High risk: score < 60 5.3 Report Generation Use Matplotlib to plot the fitted curve (scatter plot + logistic regression curve) and mark the d0 position.

[0065] Generate an HTML report that includes: a summary of the seed problem, the number of valid variants, the fitted parameters d0 and k and their confidence intervals, the content security score, and the risk level.

[0066] It also exports detailed data in CSV format (edit distance and violation probability for each variant).

[0067] This invention's scoring is entirely based on the model's actual performance data (logistic regression results), requiring no manual setting of scoring rules, and the output is statistically significant (confidence interval), increasing the reliability of the scoring. like Figure 2 As shown, the system of the present invention includes: The seed question builder is used to collect raw text from multiple public data sources via web crawlers, and then perform deduplication using MinHash local sensitive hashing, DBSCAN clustering, perplexity quality filtering based on the GPT-2 model, and class balancing to generate a seed question set covering multiple security categories. An adversarial variant generator is used to generate multiple adversarial variants for each seed problem in the seed problem set using both regular variant generation and generative variant generation methods. The regular variant generation method includes at least one of synonym substitution, sentence transformation, and noise injection, while the generative variant generation method includes at least one of BERT mask prediction and embedding space perturbation. The validity filter is used to input each adversarial variant into the content security model under test, obtain the security confidence or violation probability output by the content security model under test for each variant, calculate the decrease in confidence or change in violation probability of each variant relative to its corresponding seed problem, and mark variants with changes exceeding a preset threshold as valid variants. The logistic regression fitter is used to calculate and normalize the edit distance between each effective variant and its corresponding seed problem. The normalized edit distance is used as a measure of adversarial strength. The average violation probability of effective variants within each adversarial strength interval is calculated. The logistic regression model is used to fit the S-shaped curve relationship between adversarial strength and violation probability. The crash threshold parameter is extracted as a security vulnerability index based on the fitted curve. The scoring and reporting generator calculates a content safety score based on the number of valid variants and the crash threshold parameter, classifies the risk level based on the content safety score, and generates a test report that includes a fitted curve, fitted parameters and their confidence intervals, the number of valid variants, the content safety score, and the risk level.

[0068] Based on the above-described content security testing method and system based on variant generation and logistic regression, tests were conducted to verify the effectiveness of the present invention.

[0069] Example 1: Automated content security testing of a large model API 1. Test Objective The model under test is Baidu Wenxin Yiyan API (ernie-bot-3.5), with the interface address https: / / aip.baidubce.com / rpc / 2.0 / ai_custom / v1 / wenxinworkshop / chat / completions.

[0070] 2. Step 1: Seed Problem Construction We crawled security forums and compliance documents, obtaining a total of 2,150 raw text entries.

[0071] After deduplication using MinHash, 980 records remain.

[0072] DBSCAN clustering (eps=0.5, min_samples=3) yielded 80 clusters. One line was randomly selected from each cluster to obtain 80 seeds.

[0073] Quality filtering removes text with a perplexity score greater than 150, leaving 62 texts.

[0074] Manually supplemented to 200 (balanced according to 10 categories), resulting in a final seed problem of 200.

[0075] 3. Step 2: Adversarial Variant Generation For each seed problem, generate 5 variants: 3 rule variants (synonym substitution, sentence transformation, noise injection) + 2 generative variants (BERT mask prediction, embedding perturbation).

[0076] There are a total of 1000 variations.

[0077] 4. Step 3: Variant Validity Screening For each variant, call the Wenxin Yiyan API to get the response and calculate the violation probability (using the local RoBERTa classifier).

[0078] The average probability of violation for the seed problem is p_seed = 0.12.

[0079] The confidence level decrease or violation rate increase for each variant was calculated. A total of 312 valid variants were selected (accounting for 31.2%).

[0080] 5. Step 4: Logistic Regression Fitting Calculate the edit distance between each valid variant and the seed problem, and normalize it to obtain d (0~1).

[0081] The effective variants are divided into 10 intervals according to d, and the average violation probability R(d) in each interval is calculated.

[0082] The following was obtained using curve_fit: d0 = 0.62 (crash threshold, 95% confidence interval [0.58, 0.66]) k = 8.5 (sensitivity coefficient) Goodness of fit: R 2 = 0.94.

[0083] 6. Step 5: Content Security Scoring The score is 100 * (1 - 0.62) = 38 points, which is rated as high risk.

[0084] The generated report includes the fitted curve, d0 value, score, and rectification suggestions.

[0085] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. An automated content security testing method based on variant generation and logistic regression, characterized in that, Performed by a computer system, including the following steps: Step S1: Collect raw text from multiple public data sources through web crawling, and generate a seed question set covering multiple security categories by using MinHash local sensitive hashing for deduplication, DBSCAN clustering to select representative samples, perplexity quality filtering based on the GPT-2 model, and class balancing. Step S2: For each seed problem in the seed problem set, generate multiple adversarial variants using both regular variant generation and generative variant generation methods. The regular variant generation method includes at least one of synonym substitution, sentence transformation, and noise injection. The generative variant generation method includes at least one of BERT mask prediction and embedding space perturbation. Step S3: Input each adversarial variant into the content security model under test, obtain the security confidence or violation probability output by the content security model under test for each variant, calculate the decrease in confidence or change in violation probability of each variant relative to its corresponding seed problem, and mark variants with changes exceeding a preset threshold as valid variants. Step S4: Logistic regression fitting and security vulnerability index calculation. Calculate the edit distance between each effective variant and its corresponding seed problem and normalize it. Use the normalized edit distance as the adversarial strength measure. Statistically calculate the average violation probability of effective variants within each adversarial strength interval. Use the logistic regression model to fit the S-shaped curve relationship between adversarial strength and violation probability. Extract the collapse threshold parameter as the security vulnerability index based on the fitted curve. Step S5: Calculate the content security score based on the number of valid variants and the crash threshold parameter, classify the risk level based on the content security score, and generate a test report.

2. The content security automated testing method based on variant generation and logistic regression according to claim 1, characterized in that, The automatic construction of the seed problem in step S1 specifically includes: Step S1.1: Use a web crawler to collect raw text from the vulnerability disclosure section of the security forum, the AI ​​service violation case report document, and the open source security test set, and extract sentences with a length between 10 and 200 characters to form the original problem pool; Step S1.2: Perform word segmentation on each text in the original problem pool, convert the word segmentation results into a set, calculate the MinHash value of each text, and use the Local Sensitive Hash Index to merge and remove duplicate texts with similarity greater than a preset threshold. Step S1.3: Use a sentence encoding model to encode each deduplicated text into a vector, apply the DBSCAN clustering algorithm to cluster the encoded vectors, and select representative texts from each cluster as candidate seeds; Step S1.4: Load the pre-trained language model to calculate the perplexity of each candidate seed, and delete texts with perplexity greater than a preset threshold; Step S1.5: Label the remaining candidate seeds as multiple preset security categories, and ensure that the number of samples in each category is not less than a preset lower limit, to obtain the final seed question set.

3. The automated content security testing method based on variant generation and logistic regression according to claim 1, characterized in that, The rule variant generation method in step S2 includes: Synonym replacement: Load the pre-trained Word2Vec model, find the top N most similar words for each word in the seed question, and randomly replace one of the words with a preset probability; Sentence transformation: Convert declarative sentences into rhetorical questions by adding a rhetorical question marker at the beginning of the sentence, adding an interrogative particle at the end of the sentence, and reversing the negation of the predicate; and by randomly inserting punctuation marks between participles; Noise injection: Randomly inject noise at the character level, including inserting unknown markers at random positions with a preset probability, randomly repeating the previous character with a preset probability, and replacing numeric characters with homophone Chinese characters.

4. The automated content security testing method based on variant generation and logistic regression according to claim 1, characterized in that, The generative variant generation method in step S2 includes: BERT Mask Prediction: Load the pre-trained BERT model, replace the tokens in the seed question with mask markers according to a preset ratio, call the BERT model to predict the most likely word at the masked position, and generate new text; Embedding space perturbation: The seed problem is encoded into a vector using a pre-trained RoBERTa model. Gaussian noise is added to the vector. The word vector space of the training set is searched for the K word vectors that are closest to the perturbated vector. Beam search is used to decode and generate a new sequence.

5. The automated content security testing method based on variant generation and logistic regression according to claim 1, characterized in that, The automatic variant validity screening in step S3 specifically includes: Step S3.1: Call the API interface of the content security model under test for each sub-problem and its corresponding adversarial variant, and obtain the response text output by the model; Step S3.2: If the API return result contains a security category and its probability, directly read the security confidence or violation probability; if the API only returns the response text, use the locally pre-trained classifier to score the response text and output the violation probability. Step S3.3: Calculate the confidence decrease for each variant, confidence decrease = (safe confidence of the seed problem - safe confidence of the variant) / safe confidence of the seed problem; or calculate the change in violation probability; Step S3.4: When the confidence level decreases by more than the first threshold or the change in the probability of violation meets the preset conditions, mark the variant as a valid variant and add it to the set of valid variants.

6. The automated content security testing method based on variant generation and logistic regression according to claim 1, characterized in that, The logistic regression fitting and security vulnerability index calculation in step S4 specifically include: Step S4.1: For each valid variant, use the Levenshtein distance algorithm to calculate its edit distance with the corresponding seed problem, and normalize it by dividing by the maximum value of the seed problem and variant text length to obtain a normalized edit distance d with a value range of [0,1]. Step S4.2: Divide the [0,1] interval into multiple equal-length subintervals, and calculate the average violation probability R(d) of all valid variants falling in each subinterval; Step S4.3: Fit the data points (d_i, R_i) to the logistic regression model using the nonlinear least squares method: R(d) = 1 / (1 + exp(-k*(d - d0))) Where d represents the normalized edit distance, R(d) represents the probability of violation when the adversarial strength is d, d0 represents the crash threshold parameter, and k represents the sensitivity coefficient; Step S4.4: Extract the fitted parameter d0 as the security vulnerability index V. The smaller the value of d0, the easier it is for the security model of the content under test to produce illegal outputs under low adversarial strength.

7. The automated content security testing method based on variant generation and logistic regression according to claim 1, characterized in that, The content security comprehensive score generation in step S5 specifically includes: Step S5.1: Determine if the number of valid variants M is zero. If M=0, the content security model under test is determined to be completely secure, and the content security score is directly set to 100. Step S5.2: If M>0, then calculate the content security score based on the crash threshold parameter d0: score = 100 × (1 -d0); Step S5.3: Classify risk levels based on content security scores: a score greater than or equal to the first score threshold is low risk, a score greater than or equal to the second score threshold but less than the first score threshold is medium risk, and a score less than the second score threshold is high risk. Step S5.4: Generate a test report that includes the fitted curve, fitted parameters and their confidence intervals, number of valid variants, content security score, and risk level.

8. A test system for an automated content security testing method based on variant generation and logistic regression as described in any one of claims 1 to 7, characterized in that, include: The seed question builder is used to collect raw text from multiple public data sources via web crawlers, and then perform deduplication using MinHash local sensitive hashing, DBSCAN clustering, perplexity quality filtering based on the GPT-2 model, and class balancing to generate a seed question set covering multiple security categories. An adversarial variant generator is used to generate multiple adversarial variants for each seed problem in the seed problem set using both regular variant generation and generative variant generation methods. The regular variant generation method includes at least one of synonym substitution, sentence transformation, and noise injection, while the generative variant generation method includes at least one of BERT mask prediction and embedding space perturbation. The validity filter is used to input each adversarial variant into the content security model under test, obtain the security confidence or violation probability output by the content security model under test for each variant, calculate the decrease in confidence or change in violation probability of each variant relative to its corresponding seed problem, and mark variants with changes exceeding a preset threshold as valid variants. The logistic regression fitter is used to calculate and normalize the edit distance between each effective variant and its corresponding seed problem. The normalized edit distance is used as a measure of adversarial strength. The average violation probability of effective variants within each adversarial strength interval is calculated. The logistic regression model is used to fit the S-shaped curve relationship between adversarial strength and violation probability. The crash threshold parameter is extracted as a security vulnerability index based on the fitted curve. The scoring and reporting generator calculates a content safety score based on the number of valid variants and the crash threshold parameter, classifies the risk level based on the content safety score, and generates a test report that includes a fitted curve, fitted parameters and their confidence intervals, the number of valid variants, the content safety score, and the risk level.

9. The detection system for an automated content security testing method based on variant generation and logistic regression as described in claim 8, characterized in that, The seed problem builder includes: The web crawler module is used to collect raw text from the vulnerability disclosure section of the security forum, the AI ​​service violation case report document, and the open source security test set, and extract sentences with a length between 10 and 200 characters to form the raw question pool; The MinHash deduplication module is used to perform word segmentation on each text in the original question pool, convert the word segmentation results into a set, calculate the MinHash value of each text, and use the local sensitive hash index to merge and deduplicate texts with similarity greater than a preset threshold. The DBSCAN clustering module is used to encode each deduplicated text into a vector using a sentence encoding model, apply the DBSCAN clustering algorithm to cluster the encoded vectors, and select representative texts from each cluster as candidate seeds. The quality filtering module is used to load the pre-trained language model to calculate the perplexity of each candidate seed and delete text with a perplexity greater than a preset threshold. The category balancing module is used to label the remaining candidate seeds into multiple preset safe categories and ensure that the number of samples in each category is not less than a preset lower limit, thus obtaining the final seed question set.

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