Security code automatic generation system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an automatic security code generation system based on artificial intelligence, which comprises a demand understanding and security analysis unit, a code generation and optimization unit, a security verification and vulnerability detection unit and a feedback learning and knowledge updating unit. Through scene-based security level dynamic adaptation, reinforcement learning-driven security-performance balance and modular security component integration, it can be ensured that the generated code meets the service scene security requirement, redundant logic can be eliminated, the execution efficiency is considered, the problem that security and performance are difficult to cooperate in traditional code generation is solved, and the code generation efficiency is improved. Sustainable evolution and prospective defense of system security capability are realized, code generation is upgraded from passive compliance to active defense iteration, and the security, efficiency and adaptability of code generation are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular to a security code automatic generation system based on artificial intelligence. BACKGROUND

[0002] Artificial intelligence (AI) refers to the scientific and technological field of simulating human intelligent behavior through a computer system. The core is to enable machines to have cognitive abilities that originally require humans to complete, such as understanding language, learning knowledge, analyzing data, reasoning and decision-making, recognizing images / sounds, solving complex problems, etc. Ultimately, it realizes the functions of "perception, thinking, action, and evolution" of human-like intelligence. The goal is to enable machines to more efficiently assist or replace humans in handling various tasks, and it has been widely applied in voice assistants, image recognition, autonomous driving, intelligent medical diagnosis and many other fields. It also provides the core driving force for the innovation of code generation technology. In the field of software development, code generation technology is a key direction to improve development efficiency. In the prior art, the traditional code generation technology has the problems of "poor adaptability of security policy to business scenarios, difficulty in balancing security attributes and execution efficiency, reliance on rule library for vulnerability detection, and inability to continuously evolve system security capabilities and stay at the passive compliance level".

[0003] Therefore, the present application provides a security code automatic generation system based on artificial intelligence to solve the above technical problems. SUMMARY

[0004] The present application aims to provide a security code automatic generation system based on artificial intelligence. The present application dynamically adapts to the scene security level, enhances the safety-performance balance driven by reinforcement learning, and integrates modular security components. It can ensure that the generated code meets the security requirements of the business scenario, eliminate redundant logic, and balance execution efficiency. It solves the problem of the difficulty in coordinating safety and performance in traditional code generation. It relies on program semantic modeling and adversarial reasoning to actively excavate potential logic vulnerabilities and new defects from the perspective of attackers, breaks through the limitations of missed detection caused by traditional rule library dependence, significantly improves the depth of code security verification, and locates the root cause of vulnerabilities through causal reasoning, optimizes the model through incremental training, and integrates real-time threat intelligence to deduce potential attack defense schemes. It realizes the continuous evolution and forward-looking defense of system security capabilities, upgrades code generation from "passive compliance" to "active defense iteration", and significantly improves the safety, efficiency and adaptability of code generation.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] This invention provides an artificial intelligence-based automatic security code generation system, comprising a requirements understanding and security analysis unit, a code generation and optimization unit, a security verification and vulnerability detection unit, and a feedback learning and knowledge update unit, wherein:

[0007] The requirement understanding and security analysis unit is used to parse user requirements through natural language processing and formal methods, and simultaneously perform security policy modeling and threat identification.

[0008] The code generation and optimization unit: Based on a deep learning model, combined with security policy modeling results and scenario-based security level requirements, it generates high-quality source code through a dynamic security strength adaptation mechanism, integrates code style optimization, performance tuning and modular security logic enhancement, and dynamically balances security attributes and execution efficiency.

[0009] The security verification and vulnerability detection unit is used to construct a program semantic behavior graph and combine it with an adversarial reasoning engine to simulate the attacker's thinking to perform multi-step security deduction on the control flow, data flow and state transition of the code, and actively identify potential logical vulnerabilities and unknown security flaws.

[0010] The feedback learning and knowledge update unit is used to proactively evolve and proactively defend the system's security capabilities by driving feedback distillation and vulnerability deduction through causal reasoning.

[0011] The requirements understanding and security analysis unit includes a requirements parsing module, a security policy modeling module, and a threat identification module, wherein:

[0012] The requirement parsing module is used to transform unstructured user requirements into structured functional descriptions using natural language processing technology, and to extract core business objectives and constraints.

[0013] The security policy modeling module transforms security requirements into executable policy rules based on industry security standards, defining security boundaries for data encryption and access control.

[0014] The threat identification module is used to combine the threat intelligence database and the attack tree model to predict potential attack vectors in the required scenarios and mark the risk level.

[0015] The code generation and optimization unit includes a scenario adaptation module, a code generation engine module, a security-performance optimization module, and a modular security integration module, wherein:

[0016] The scenario adaptation module is used to dynamically adjust the strength of security policies based on the security level quantification indicators and establish a mapping relationship between scenario and code security features.

[0017] The code generation engine module generates initial code that conforms to syntax rules and embeds basic security logic, based on the pre-trained large code model and the security policy modeling results.

[0018] The security-performance optimization module is used to balance encryption complexity, security attributes of verification levels, and code execution efficiency through reinforcement learning algorithms, and to eliminate redundant security logic.

[0019] The modular security integration module is used to decompose the security functions of data anonymization and log auditing into reusable security components, which can be combined and embedded into the generated code as needed.

[0020] The scenario adaptation module dynamically adjusts the security policy strength based on the security level quantification index, and establishes a mapping relationship between scenario and code security features. The specific operation is as follows:

[0021] A1: Construct a quantitative index system for security levels, and calculate the scenario security level value S using the following formula:

[0022] S=α×D+β×B+γ×C

[0023] Where D is the data sensitivity coefficient, B is the business risk coefficient, C is the compliance requirement coefficient, α, β, and γ are the scenario layer weight coefficients, and α+β+γ=1;

[0024] A2: Preset security policy strength level threshold:

[0025] ① When S∈[0.1,0.4), it is at the basic level;

[0026] ② When S∈[0.4,0.7), it is an enhancement level;

[0027] ③ When S∈[0.7,1.0], it is the highest level;

[0028] A3: Match the corresponding security policy strength based on the scenario security level value S:

[0029] ① The basic-level policy enables "data transmission encryption + basic access control";

[0030] ②The enhanced strategy adds "operation log auditing + regular vulnerability scanning" to the basic level;

[0031] ③ The highest level strategy superimposes "multi-factor authentication + real-time intrusion detection" on top of the enhanced level;

[0032] A4: Establish a mapping table between scene features and security level values ​​S, and automatically match scene inputs to security policies.

[0033] The security-performance optimization module uses reinforcement learning algorithms to balance encryption complexity, security attributes of verification levels, and code execution efficiency, while eliminating redundant security logic. The specific operations are as follows:

[0034] B1: Define reinforcement learning environment parameters:

[0035] ①State space S: contains security attribute parameters and performance parameters. Among them, the security attribute parameters include encryption algorithm complexity E and verification level L, and the performance parameters include code execution delay T and resource utilization rate R.

[0036] ② Action Space A: Includes encryption strength adjustment, addition or removal of verification steps, and elimination of redundant logic;

[0037] ③Reward function R: Where T0 and R0 are performance baseline values, E0 and L0 are security baseline values, C is the number of redundant logic, λ1-λ4 are the optimization layer weight coefficients, and λ1+λ2+λ3+λ4=1;

[0038] B2: Initialize Security-Performance Strategy: Based on the security level output by the scenario adaptation module, set the initial encryption complexity E1 and the verification level L1;

[0039] B3: Iterative Optimization

[0040] ① Execute the code generated by the current strategy and collect the actual performance parameters T, R and the number of redundant logic C;

[0041] ② Calculate the reward value R. If R < threshold θ, trigger policy adjustment and select the optimal action from the action space A using the ε-greedy algorithm.

[0042] ③ Repeat the iteration until the reward value R reaches the preset convergence condition above the threshold θ, and output the optimal safety-performance balance strategy;

[0043] B4: Redundant logic removal rule: When the execution frequency of a certain security verification step is less than 0.1 times / second and the security attribute degradation rate after its removal is less than 5%, it is marked as redundant logic and automatically removed.

[0044] The security verification and vulnerability detection unit includes a program semantic modeling module, an adversarial reasoning module, an unknown defect mining module, and a verification report generation module, wherein:

[0045] The program semantic modeling module is used to construct the control flow, data flow, and state transition diagram of the code, and to parse the program execution logic and variable dependencies.

[0046] The adversarial reasoning module is used to simulate the attacker's thought process, perform multi-step attack deduction on the code through symbolic execution technology, and identify the triggering conditions of logical vulnerabilities.

[0047] The unknown defect mining module analyzes the semantic deviation of the code based on anomaly detection algorithms to discover new security defects not covered by traditional rule bases.

[0048] The verification report generation module is used to integrate vulnerability locations and generate a visual detection report containing remediation suggestions.

[0049] The adversarial reasoning module simulates the attacker's thought process and uses symbolic execution technology to perform multi-step attack deduction on the code, identifying the triggering conditions for logical vulnerabilities. The specific operations are as follows:

[0050] C1: Storage attack patterns, exploit chains, and common vulnerability triggering conditions;

[0051] C2: Abstracts and interprets program code, converts program input into symbolic values, and collects constraints on the program path;

[0052] C3: Analyze program semantics and automatically generate attack hypotheses to be verified, such as "Can the user obtain unauthorized access?", based on the attack knowledge base.

[0053] C4: A collaborative symbolic execution engine and attack knowledge base that derives a series of program execution paths and input conditions required to satisfy attack hypotheses.

[0054] The feedback learning and knowledge update unit includes a feedback data processing module, a model fine-tuning module, a threat intelligence fusion module, and a proactive defense module, wherein:

[0055] The feedback data processing module is used to locate the root cause in the code generation process by analyzing vulnerability repair records and user feedback through causal reasoning.

[0056] The model fine-tuning module: performs incremental training on the code generation model based on vulnerability characteristics and remediation experience to optimize the security logic generation capability;

[0057] The threat intelligence fusion module is used to integrate new attack patterns and vulnerability databases in real time, and to update the system's threat identification and defense strategy database.

[0058] The forward-looking defense module is used to generate defense code templates against potential attacks in advance by deducing the vulnerability evolution path.

[0059] The feedback data processing module uses causal reasoning to analyze vulnerability repair records and user feedback to pinpoint the root cause in the code generation process. The specific operations are as follows:

[0060] D1: Clean, normalize, and correlate the collected vulnerability remediation records and user feedback to build a structured causal analysis dataset;

[0061] D2: By applying causal discovery algorithms to the dataset, generate a graphical model representing the causal relationships between various factors and vulnerabilities in the code generation process;

[0062] D3: By traversing and analyzing the cause-effect graph model, identify the key root cause nodes and their causal paths that contribute the most to the vulnerability.

[0063] D4: Map the inferred root cause nodes and their paths back to the specific code generation stage, and output actionable optimization suggestions.

[0064] The proactive defense module generates defense code templates against potential attacks in advance by deducing vulnerability evolution paths. The specific operation is as follows:

[0065] E1: Analyze historical vulnerability data and attack pattern sequences to construct vulnerability evolution chains and predict potential future attack vectors;

[0066] E2: Derive the corresponding defense principles and logical requirements based on the predicted future attack vectors;

[0067] E3: Transform defense principles and logical requirements into secure code templates for specific programming languages;

[0068] E4: Inject the newly synthesized security code template into the system's security knowledge base for the code generation and optimization unit to use during the development phase.

[0069] Compared with the prior art, the beneficial effects of the present invention are:

[0070] This invention addresses the challenge of balancing security and performance in traditional code generation by dynamically adapting security levels to specific scenarios, using reinforcement learning to drive a balance between security and performance, and integrating modular security components. This ensures generated code meets the security requirements of business scenarios while eliminating redundant logic and maintaining execution efficiency. Furthermore, leveraging program semantic modeling and adversarial reasoning, it proactively uncovers potential logical vulnerabilities and novel defects from an attacker's perspective, overcoming the limitations of traditional rule-based databases that lead to missed detections and significantly improving the depth of code security verification. Moreover, by using causal reasoning to pinpoint the root causes of vulnerabilities, incrementally training and optimizing models, and integrating real-time threat intelligence to deduce potential attack defense schemes, it achieves continuous evolution and proactive defense of system security capabilities. This transforms code generation from "passive compliance" to "proactive defense iteration," significantly improving the security, efficiency, and adaptability of code generation. Attached Figure Description

[0071] Fig. 1 This is a system diagram of the AI-based automatic security code generation system of the present invention.

[0072] Fig. 2 This is a flowchart illustrating the reinforcement learning optimization process in the AI-based automatic security code generation system of this invention.

[0073] Fig. 3 This is a flowchart illustrating the learning and evolution process in the AI-based automatic security code generation system of this invention.

[0074] Explanation of icon numbers:

[0075] 100. Requirements Understanding and Security Analysis Unit; 101. Requirements Parsing Module; 102. Security Policy Modeling Module; 103. Threat Identification Module; 200. Code Generation and Optimization Unit; 201. Scenario Adaptation Module; 202. Code Generation Engine Module; 203. Security-Performance Optimization Module; 204. Modular Security Integration Module; 300. Security Verification and Vulnerability Detection Unit; 301. Program Semantic Modeling Module; 302. Adversarial Reasoning Module; 303. Unknown Defect Discovery Module; 304. Verification Report Generation Module; 400. Feedback Learning and Knowledge Update Unit; 401. Feedback Data Processing Module; 402. Model Fine-tuning Module; 403. Threat Intelligence Fusion Module; 404. Proactive Defense Module. Detailed Implementation

[0076] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0077] Example:

[0078] like Figs. 1-3As shown, this embodiment provides an AI-based automatic security code generation system, including a requirements understanding and security analysis unit 100, a code generation and optimization unit 200, a security verification and vulnerability detection unit 300, and a feedback learning and knowledge update unit 400. Specifically: the requirements understanding and security analysis unit 100 analyzes user requirements using natural language processing and formal methods, and simultaneously performs security policy modeling and threat identification; the code generation and optimization unit 200, based on a deep learning model, combines security policy modeling results with scenario-based security level requirements, generates high-quality source code through a dynamic security strength adaptation mechanism, integrates code style optimization, performance tuning, and modular security logic enhancement, and dynamically balances security attributes and execution efficiency; the security verification and vulnerability detection unit 300 constructs a program semantic behavior graph and combines it with an adversarial inference engine to simulate attacker thinking on the code's control flow, data flow, and state transitions through multi-step security deduction, proactively identifying potential logical vulnerabilities and unknown security flaws; and the feedback learning and knowledge update unit 400 drives feedback distillation and vulnerability deduction through causal reasoning, proactively evolving and proactively defending the system's security capabilities.

[0079] It should be noted that the output of the requirement understanding and security analysis unit 100 drives the generation process of the code generation and optimization unit 200. The code produced is subjected to in-depth security analysis by the security verification and vulnerability detection unit 300. The vulnerabilities and feedback discovered in this step are then input into the feedback learning and knowledge update unit 400. The resulting evolved security knowledge then feeds back into the requirement analysis of the requirement understanding and security analysis unit 100 and the code generation of the code generation of the code generation and optimization unit 200.

[0080] In this embodiment, it should also be noted that the requirement understanding and security analysis unit 100 includes a requirement parsing module 101, a security policy modeling module 102, and a threat identification module 103, wherein: the requirement parsing module 101 is used to transform unstructured user requirements into structured functional descriptions through natural language processing technology, and extract core business objectives and constraints; the security policy modeling module 102 is used to transform security requirements into executable policy rules based on industry security standards, and define the security boundaries of data encryption and access control; the threat identification module 103 is used to combine a threat intelligence database and an attack tree model to predict potential attack vectors in the requirement scenario and mark the risk level.

[0081] It should be noted that after the requirements analysis module 101 transforms user requirements into a structured functional description, it provides a basis for the security policy modeling module 102 to generate executable security policy rules. At the same time, it provides an analysis context for the threat identification module 103, which combines threat intelligence to predict attack vectors and mark risks. Its output results further revise and strengthen the formulation of security policies.

[0082] Furthermore, it should be noted that the natural language processing technology in the requirements analysis module 101 adopts a "pre-trained language model (such as BERT) + domain fine-tuning" approach to perform word segmentation, part-of-speech tagging, and dependency parsing on user requirements. Through the Prompt engineering-guided model, core business objectives (such as "the financial transaction system needs to support 100,000 concurrent users") and constraints (such as "user passwords need to be encrypted and stored") are extracted and transformed into a JSON structured description. In the security policy modeling module 102, industry security standard libraries (such as PCIDSS for the financial industry and HIPAA for the medical industry) automatically match security rules (such as those for financial security standards) to the requirements analysis results. The scenario triggers conditions such as "data encryption algorithm must be ≥AES-256" and "access control must comply with the principle of least privilege," generating executable policies (such as code comments and configuration file fragments) for direct use by the code generation engine. The attack tree model construction logic in threat identification module 103 is as follows: taking a "financial transaction system" as an example, the root node is "damaging transaction security," and the child nodes are decomposed into "stealing user credentials," "tampering with transaction data," and "denial-of-service attack." Each child node is further refined into attack vectors (e.g., "stealing user credentials" includes "phishing attack," "brute-force attack," and "session hijacking"), combined with the threat intelligence database to mark the risk level (CVSS score).

[0083] In this embodiment, it should also be noted that the code generation and optimization unit 200 includes a scenario adaptation module 201, a code generation engine module 202, a security-performance optimization module 203, and a modular security integration module 204, wherein: the scenario adaptation module 201 is used to dynamically adjust the security policy strength according to the security level quantification index and establish a mapping relationship between scenario and code security features; the specific operation is as follows: A1: Construct a security level quantification index system and calculate the scenario security level value S, the formula is:

[0084] S=α×D+β×B+γ×C

[0085] Where D is the data sensitivity coefficient, B is the business risk coefficient, C is the compliance requirement coefficient, and α, β, and γ are the scenario layer weight coefficients, and α+β+γ=1; A2: Preset security policy strength level thresholds: ① When S∈[0.1,0.4), it is the basic level; ② When S∈[0.4,0.7), it is the enhanced level; ③ When S∈[0.7,1.0], it is the highest level; A3: Match the corresponding security policy strength based on the scenario security level value S: ① The basic level policy enables "data transmission encryption + basic access control"; ② The enhanced level policy adds "operation log auditing + periodic vulnerability scanning" to the basic level; ③ The highest level policy superimposes "multi-factor authentication + real-time intrusion detection" on the enhanced level; A4: Establish a mapping table between scenario features and security level value S, and automatically match scenario inputs to security policies. Code generation engine module 202: Based on the pre-trained large code model and security policy modeling results, it generates initial code that conforms to syntax specifications and embeds basic security logic; Security-performance optimization module 203: Used to balance encryption complexity, security attributes of verification levels, and code execution efficiency through reinforcement learning algorithms, and eliminate redundant security logic; Specific operations are as follows: B1: Define reinforcement learning environment parameters: ① State space S: Includes security attribute parameters and performance parameters, where security attribute parameters include encryption algorithm complexity E and verification level L, and performance parameters include code execution latency T and resource utilization rate R; ② Action space A: Includes encryption strength adjustment, addition or removal of verification steps, and elimination of redundant logic; ③ Reward function R: Where T0 and R0 are performance benchmark values, E0 and L0 are security benchmark values, C is the number of redundant logic, λ1-λ4 are optimization layer weight coefficients, and λ1+λ2+λ3+λ4=1; B2: Initialize security-performance strategy: Based on the security level output by the scenario adaptation module 201, set the initial encryption complexity E1 and verification level L1; B3: Iterative optimization: ① Execute the code to generate the current strategy, collect the actual performance parameters T, R and the number of redundant logic C; ② Calculate the reward value R. If R < threshold θ, trigger the strategy adjustment and select the optimized action from the action space A through the ε-greedy algorithm; ③ Repeat the iteration until the reward value R reaches the preset convergence condition above the threshold θ, and output the optimal security-performance balance strategy; B4: Redundant logic removal rule: When the execution frequency of a certain security verification step is less than 0.1 times / second and the security attribute decrease rate after its removal is <5%, it is marked as redundant logic and automatically removed. Modular security integration module 204: Used to decompose the security functions of data anonymization and log auditing into reusable security components, which can be combined and embedded as needed to generate code.

[0086] It should be noted that the scenario adaptation module 201 dynamically determines the security level and matches the policy strength based on quantitative indicators, providing generation constraints for the code generation engine module 202 to produce initial code. This code is then used by the security-performance optimization module 203 to iteratively balance security and efficiency and eliminate redundant logic through reinforcement learning. Finally, the modular security integration module 204 embeds reusable security components as needed.

[0087] Furthermore, it should be noted that the preset convergence conditions are: the reward value R fluctuates by less than 0.01 for three consecutive iterations, the rate of decrease in security attributes is less than 3%, and the performance improvement rate is greater than 5%, to ensure that the optimized strategy is stable and effective.

[0088] In this embodiment, it should also be noted that the security verification and vulnerability detection unit 300 includes a program semantic modeling module 301, an adversarial reasoning module 302, an unknown defect mining module 303, and a verification report generation module 304. Specifically: the program semantic modeling module 301 is used to construct the control flow, data flow, and state transition diagram of the code, and to parse the program execution logic and variable dependencies; the adversarial reasoning module 302 is used to simulate the attacker's thought process, perform multi-step attack deduction on the code using symbolic execution technology, and identify logical vulnerability triggering conditions; the specific operations are as follows: C1: Store attack modes, vulnerability exploitation chains, and common vulnerability triggering conditions; C2: Abstract and interpret the program code, convert program inputs into symbolic values, and collect constraints on the program path; C3: Analyze program semantics and automatically generate attack hypotheses to be verified, such as "Can a user obtain unauthorized access?", based on the attack knowledge base; C4: Collaborate with the symbolic execution engine and the attack knowledge base to deduce a series of program execution paths and input conditions required to satisfy the attack hypotheses. Unknown defect discovery module 303: Analyzes code semantic deviation based on anomaly detection algorithms to discover new security defects not covered by traditional rule bases; Verification report generation module 304: Used to integrate vulnerability locations and generate a visual detection report containing remediation suggestions.

[0089] It should be noted that the program semantic modeling module 301 constructs a program behavior graph by parsing code logic, providing a basis for symbolic execution and multi-step attack deduction for the adversarial reasoning module 302. The potential vulnerability paths output by the module and the semantic deviation anomalies discovered by the unknown defect mining module 303 are input together to the verification report generation module 304, and finally integrated to generate a visual security report containing vulnerability locations and remediation suggestions.

[0090] Furthermore, it should be noted that the program semantic modeling module 301 uses LLVM intermediate representation to construct control flow graphs and data flow graphs, parses variable dependencies (such as the propagation path of user input variables → function calls → database operations), and generates visual semantic graphs (such as flowcharts rendered by Graphviz), providing an "attack path map" for adversarial reasoning. Vulnerability locations include location, risk level, and exploit path.

[0091] In this embodiment, it should also be noted that the feedback learning and knowledge update unit 400 includes a feedback data processing module 401, a model fine-tuning module 402, a threat intelligence fusion module 403, and a forward-looking defense module 404. Specifically, the feedback data processing module 401 is used to locate the root cause in the code generation stage by analyzing vulnerability repair records and user feedback through causal reasoning. The specific operations are as follows: D1: Cleaning, normalizing, and correlating the collected vulnerability repair records and user feedback to construct a structured causal analysis dataset; D2: Applying a causal discovery algorithm to the dataset to generate a graph model representing the causal relationship between various factors and vulnerabilities in the code generation stage; D3: Identifying the key root cause nodes and their causal paths that contribute the most to the vulnerability by traversing and analyzing the causal graph model; D4: Mapping the inferred root cause nodes and their paths back to the specific code generation stage and outputting actionable optimization suggestions. Model fine-tuning module 402: Incrementally trains the code generation model based on vulnerability characteristics and remediation experience to optimize security logic generation capabilities; Threat intelligence fusion module 403: Integrates new attack patterns and vulnerability databases in real time to update the system's threat identification and defense strategy library; Proactive defense module 404: Generates defense code templates against potential attacks in advance through vulnerability evolution path deduction. Specific operations are as follows: E1: Analyzes historical vulnerability data and attack pattern sequences, constructs vulnerability evolution chains, and predicts potential future attack vectors; E2: Derives corresponding defense principles and logical requirements from the predicted future attack vectors; E3: Converts the defense principles and logical requirements into security code templates in specific programming languages; E4: Injects the newly synthesized security code templates into the system's security knowledge base for the code generation and optimization unit 200 to use during the development phase.

[0092] It should be noted that the feedback data processing module 401 locates the root cause of the vulnerability through causal reasoning and outputs optimization suggestions, driving the model fine-tuning module 402 to perform security enhancement training on the code generation model. At the same time, the threat intelligence fusion module 403 integrates external threat data and updates the strategy library in real time. Together, they provide an evolutionary basis for the forward-looking defense module 404, enabling it to pre-generate defense code templates and feed them back to the code generation stage.

[0093] Furthermore, it should be noted that the specific strategy for incremental training is as follows: Based on LoRA (Low-Rank Adaptation) or QLoRA technology, incremental training is performed on the pre-trained large code model (such as CodeGeeX, StarCoder): ① Input: The "root cause-remediation case" dataset output by the feedback data processing module 401 (e.g., "insufficient security policy coverage" leads to a privilege circumvention vulnerability, and the remediation solution is "supplementing the RBAC privilege verification template"). ② Training logic: The remediation cases are transformed into Prompt-Response pairs (Prompt: "Generate user login code containing RBAC privilege verification"; Response: "Call check..."). permission The `()` function verifies whether the user role is "admin" and is injected into the model training to allow the model to learn the "correct pattern for generating security logic".

[0094] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0095] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An AI-based automatic security code generation system, characterized in that, It includes a requirements understanding and security analysis unit (100), a code generation and optimization unit (200), a security verification and vulnerability detection unit (300), and a feedback learning and knowledge update unit (400), wherein: The requirement understanding and security analysis unit (100) is used to parse user requirements through natural language processing and formal methods, and simultaneously perform security policy modeling and threat identification. The code generation and optimization unit (200) is based on a deep learning model, combines the security strategy modeling results with the scenario-based security level requirements, generates high-quality source code through a dynamic security strength adaptation mechanism, integrates code style optimization, performance tuning and modular security logic enhancement, and dynamically balances security attributes and execution efficiency. The security verification and vulnerability detection unit (300) is used to construct a program semantic behavior graph and combine it with an adversarial reasoning engine to simulate the attacker's thinking to perform multi-step security deduction on the control flow, data flow and state transition of the code, and actively identify potential logical vulnerabilities and unknown security defects. The feedback learning and knowledge update unit (400) is used to proactively evolve and proactively defend the system's security capabilities by driving feedback distillation and vulnerability deduction through causal reasoning.

2. The AI-based automatic security code generation system according to claim 1, characterized in that, The requirement understanding and security analysis unit (100) includes a requirement parsing module (101), a security policy modeling module (102), and a threat identification module (103), wherein: The requirement parsing module (101) is used to transform unstructured user requirements into structured functional descriptions through natural language processing technology, and to extract core business objectives and constraints. The security policy modeling module (102) transforms security requirements into executable policy rules based on industry security standards, and defines the security boundaries for data encryption and access control. The threat identification module (103) is used to combine the threat intelligence database and the attack tree model to predict potential attack vectors in the required scenario and mark the risk level.

3. The AI-based automatic security code generation system according to claim 1, characterized in that, The code generation and optimization unit (200) includes a scene adaptation module (201), a code generation engine module (202), a security-performance optimization module (203), and a modular security integration module (204), wherein: The scenario adaptation module (201) is used to dynamically adjust the strength of security policies according to the security level quantification indicators and establish a mapping relationship between scenario and code security features. The code generation engine module (202) generates initial code that conforms to syntax rules and embeds basic security logic based on the pre-trained large code model and security policy modeling results; The security-performance optimization module (203) is used to balance the security attributes of encryption complexity and verification level with code execution efficiency through reinforcement learning algorithms, and to eliminate redundant security logic. The modular security integration module (204) is used to decompose the security functions of data anonymization and log auditing into reusable security components, which are then combined and embedded into the generated code as needed.

4. The AI-based automatic security code generation system according to claim 3, characterized in that, The scenario adaptation module (201) dynamically adjusts the security policy strength based on the security level quantification index and establishes a mapping relationship between scenario and code security features. The specific operation is as follows: A1: Construct a quantitative index system for security levels, and calculate the scenario security level value S using the following formula: S=α×D+β×B+γ×C Where D is the data sensitivity coefficient, B is the business risk coefficient, C is the compliance requirement coefficient, α, β, and γ are the scenario layer weight coefficients, and α+β+γ=1; A2: Preset security policy strength level threshold: ① When S∈[0.1,0.4), it is at the basic level; ② When S∈[0.4,0.7), it is an enhancement level; ③ When S∈[0.7,1.0], it is the highest level; A3: Match the corresponding security policy strength based on the scenario security level value S: ① The basic-level policy enables "data transmission encryption + basic access control"; ②The enhanced strategy adds "operation log auditing + regular vulnerability scanning" to the basic level; ③ The highest level strategy superimposes "multi-factor authentication + real-time intrusion detection" on top of the enhanced level; A4: Establish a mapping table between scene features and security level values ​​S, and automatically match scene inputs to security policies.

5. The AI-based automatic security code generation system according to claim 3, characterized in that, The security-performance optimization module (203) uses reinforcement learning algorithms to balance encryption complexity, security attributes of verification levels, and code execution efficiency, and eliminates redundant security logic. The specific operations are as follows: B1: Define reinforcement learning environment parameters: ①State space S: contains security attribute parameters and performance parameters. Among them, the security attribute parameters include encryption algorithm complexity E and verification level L, and the performance parameters include code execution delay T and resource utilization rate R. ② Action Space A: Includes encryption strength adjustment, addition or removal of verification steps, and elimination of redundant logic; ③Reward function R: Where T0 and R0 are performance baseline values, E0 and L0 are security baseline values, C is the number of redundant logic, λ1-λ4 are the optimization layer weight coefficients, and λ1+λ2+λ3+λ4=1; B2: Initialize security-performance strategy: Based on the security level output by the scenario adaptation module (201), set the initial encryption complexity E1 and the verification level L1; B3: Iterative Optimization ① Execute the code generated by the current strategy and collect the actual performance parameters T, R and the number of redundant logic C; ② Calculate the reward value R. If R < threshold θ, trigger policy adjustment and select the optimal action from the action space A using the ε-greedy algorithm. ③ Repeat the iteration until the reward value R reaches the preset convergence condition above the threshold θ, and output the optimal safety-performance balance strategy; B4: Redundant logic removal rule: When the execution frequency of a certain security verification step is less than 0.1 times / second and the security attribute degradation rate after its removal is less than 5%, it is marked as redundant logic and automatically removed.

6. The AI-based automatic security code generation system according to claim 5, characterized in that, The security verification and vulnerability detection unit (300) includes a program semantic modeling module (301), an adversarial reasoning module (302), an unknown defect mining module (303), and a verification report generation module (304), wherein: The program semantic modeling module (301) is used to construct the control flow, data flow and state transition diagram of the code, and to parse the program execution logic and variable dependencies; The adversarial reasoning module (302) is used to simulate the attacker's thought process, perform multi-step attack deduction on the code through symbolic execution technology, and identify the triggering conditions of logical vulnerabilities. The unknown defect mining module (303) analyzes the semantic deviation of the code based on the anomaly detection algorithm and discovers new security defects not covered by the traditional rule base; The verification report generation module (304) is used to integrate vulnerability locations and generate a visual detection report containing remediation suggestions.

7. The AI-based automatic security code generation system according to claim 6, characterized in that, The adversarial reasoning module (302) simulates the attacker's thought process, uses symbolic execution technology to perform multi-step attack deduction on the code, and identifies the triggering conditions of logical vulnerabilities. The specific operation is as follows: C1: Storage attack patterns, exploit chains, and common vulnerability triggering conditions; C2: Abstracts and interprets program code, converts program input into symbolic values, and collects constraints on the program path; C3: Analyze program semantics and automatically generate attack hypotheses to be verified, such as "Can the user obtain unauthorized access?", based on the attack knowledge base. C4: A collaborative symbolic execution engine and attack knowledge base that derives a series of program execution paths and input conditions required to satisfy attack hypotheses.

8. The AI-based automatic security code generation system according to claim 1, characterized in that, The feedback learning and knowledge update unit (400) includes a feedback data processing module (401), a model fine-tuning module (402), a threat intelligence fusion module (403), and a proactive defense module (404), wherein: The feedback data processing module (401) is used to locate the root cause of the code generation process by analyzing the vulnerability repair records and user feedback through causal reasoning. The model fine-tuning module (402) performs incremental training on the code generation model based on vulnerability characteristics and remediation experience to optimize the security logic generation capability; The threat intelligence fusion module (403) is used to integrate new attack patterns and vulnerability databases in real time and update the system's threat identification and defense strategy database. The forward-looking defense module (404) is used to generate defense code templates against potential attacks in advance by deducing the vulnerability evolution path.

9. The AI-based automatic security code generation system according to claim 8, characterized in that, The feedback data processing module (401) uses causal reasoning to analyze vulnerability repair records and user feedback to pinpoint the root cause in the code generation process. The specific operations are as follows: D1: Clean, normalize, and correlate the collected vulnerability remediation records and user feedback to build a structured causal analysis dataset; D2: By applying causal discovery algorithms to the dataset, generate a graphical model representing the causal relationships between various factors and vulnerabilities in the code generation process; D3: By traversing and analyzing the cause-effect graph model, identify the key root cause nodes and their causal paths that contribute the most to the vulnerability. D4: Map the inferred root cause nodes and their paths back to the specific code generation stage, and output actionable optimization suggestions.

10. The AI-based automatic security code generation system according to claim 8, characterized in that, The proactive defense module (404) generates defense code templates against potential attacks in advance by deducing the vulnerability evolution path. The specific operation is as follows: E1: Analyze historical vulnerability data and attack pattern sequences to construct vulnerability evolution chains and predict potential future attack vectors; E2: Derive the corresponding defense principles and logical requirements based on the predicted future attack vectors; E3: Transform defense principles and logical requirements into secure code templates for specific programming languages; E4: Inject the newly synthesized security code template into the system's security knowledge base for the code generation and optimization unit (200) to call during the development phase.

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