A defense method and device for a prompt word, a storage medium and an electronic device

Abstract

The application discloses a defense method and device for a prompt word, a storage medium and an electronic device, relates to the technical field of artificial intelligence security and prompt word engineering, and forms a comprehensive security protection system through cooperation. The attack feature map provides evolution knowledge for prediction and immunity. Zero-day attack prediction enables the system to prepare in advance to cope with attacks that have not yet occurred but are extremely likely to occur. Game tree search ensures the theoretical optimality of the defense strategy in the worst case. The multi-layer immune defense of the scheme takes into account innate immunity, immune memory and adaptive immunity, etc. Therefore, the scheme comprehensively improves the security of the prompt word from multiple dimensions such as attack identification, strategy optimization, rapid response and continuous evolution.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence security and prompt word engineering technology, and more specifically, to a method, apparatus, storage medium, and electronic device for defending against prompt words. Background Technology

[0002] With the widespread application of various Large Language Models (LLMs), prompts have become a core interface for human-computer interaction.

[0003] However, the security issues of prompt words are becoming increasingly serious, mainly manifested in: 1) Prompt Injection attacks, in which attackers construct special inputs to bypass the constraints of prompt words, causing the model to output content that violates expectations; 2) Jailbreak attacks, which involve inducing the model to break through security restrictions through carefully designed dialogue strategies; 3) Data leakage risks, where malicious input may lead to the model leaking training data or system prompt words; 4) Rapid evolution of attacks, where new attack methods are constantly emerging, and attackers learn from each other and improve their attack methods, resulting in the evolution of attacks.

[0004] Therefore, how to improve the security of prompt words is a problem that this application urgently needs to solve. Summary of the Invention

[0005] In view of this, this application discloses a method, apparatus, storage medium and electronic device for defending against prompt words, aiming to comprehensively improve the security of prompt words from multiple dimensions such as attack identification, strategy optimization, rapid response and continuous evolution.

[0006] To achieve the above objectives, the disclosed technical solution is as follows:

[0007] The first aspect of this application discloses a defense method against prompt words, the method comprising:

[0008] Extract multidimensional gene features from the initial prompts obtained;

[0009] An attack feature map is constructed based on the multidimensional gene features;

[0010] Zero-day attack prediction is performed using the attack feature map and graph attention network to obtain predicted attack samples;

[0011] Based on the predicted attack samples and defense action samples, construct an adversarial game tree;

[0012] The optimal defense strategy is searched from the adversarial game tree using a preset search method, and a multi-layered immune defense deployment is carried out based on the optimal defense strategy.

[0013] A second aspect of this application discloses a defense device against prompt words, the device comprising:

[0014] The extraction unit is used to extract multidimensional gene features from the acquired initial prompts;

[0015] The first construction unit is used to construct an attack feature map based on the multidimensional gene features;

[0016] The prediction unit is used to predict zero-day attacks using the attack feature map and graph attention network to obtain predicted attack samples.

[0017] The second construction unit is used to construct an adversarial game tree based on the predicted attack samples and defense action samples;

[0018] The search deployment unit is used to search for the optimal defense strategy from the adversarial game tree using a preset search method, and to deploy a multi-layered immune defense based on the optimal defense strategy.

[0019] A third aspect of this application discloses a storage medium comprising stored instructions, wherein, when the instructions are executed, the device in which the storage medium resides executes the defense method against prompt words as described in the first aspect.

[0020] The fourth aspect of this application discloses an electronic device including a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors using the defense method against prompt words as described in the first aspect.

[0021] As can be seen from the above technical solutions, this solution forms a comprehensive security protection system through synergistic effects. Attack signature mapping provides evolutionary knowledge for prediction and immunity; zero-day attack prediction enables the system to prepare in advance for attacks that have not yet occurred but are highly likely to occur; game tree search ensures the theoretical optimality of the defense strategy in the worst-case scenario; and the multi-layered immune defense of this solution takes into account innate immunity, immune memory, and adaptive immunity. This allows this solution to comprehensively improve the security of prompt words from multiple dimensions, including attack identification, strategy optimization, rapid response, and continuous evolution. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for defending against prompt words disclosed in an embodiment of this application;

[0024] Figure 2 This is an example diagram of the attack family tree disclosed in the embodiments of this application;

[0025] Figure 3 This is a schematic diagram illustrating the construction of the attack feature map disclosed in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the attack DNA coding structure disclosed in an embodiment of this application;

[0027] Figure 5 This is a flowchart of zero-day attack prediction using graph attention networks as disclosed in an embodiment of this application.

[0028] Figure 6 This is a schematic diagram illustrating the construction of an adversarial game tree as disclosed in an embodiment of this application;

[0029] Figure 7 This is a schematic diagram of the Alpha-Beta pruning search disclosed in the embodiments of this application;

[0030] Figure 8 This is a schematic diagram illustrating the Nash equilibrium solution disclosed in the embodiments of this application;

[0031] Figure 9 This is a schematic diagram of the multi-layered immune defense architecture disclosed in the embodiments of this application;

[0032] Figure 10 This is a schematic diagram of the antibody structure disclosed in the embodiments of this application;

[0033] Figure 11 This is a schematic diagram of the clonal selection and supermutation process disclosed in the embodiments of this application;

[0034] Figure 12 This is a schematic diagram of the overall architecture of the defense system against prompt words disclosed in an embodiment of this application;

[0035] Figure 13 This is a schematic diagram of a defense device against prompt words disclosed in an embodiment of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] As the background technology indicates, the security issues of prompt words are becoming increasingly serious, mainly manifested in: 1) prompt word injection attacks, in which attackers construct special inputs to bypass the constraints of prompt words, causing the model to output content that violates expectations; 2) jailbreak attacks, which involve inducing the model to break through security restrictions through carefully designed dialogue strategies; 3) data leakage risks, where malicious input may lead to the model leaking training data or system prompt words; and 4) rapid attack evolution, where new attack methods are constantly emerging, and attackers learn from and improve their attack methods, resulting in the evolution of attacks. Therefore, how to improve the security of prompt words is a problem that this application urgently needs to solve.

[0038] To address the aforementioned issues, this application discloses a method, apparatus, storage medium, and electronic device for defending against prompt words. This solution forms a comprehensive security protection system through synergistic effects. Attack feature maps provide evolutionary knowledge for prediction and immunization; zero-day attack prediction enables the system to prepare in advance for attacks that have not yet occurred but are highly likely to occur; game tree search ensures the theoretical optimality of the defense strategy in the worst-case scenario; and the multi-layered immune defense of this solution takes into account innate immunity, immune memory, and adaptive immunity. This allows the solution to comprehensively improve the security against prompt words from multiple dimensions, including attack identification, strategy optimization, rapid response, and continuous evolution. Specific implementation methods are described in detail through the following embodiments.

[0039] It should be noted that the method, apparatus, storage medium, and electronic device for defending against prompt words provided in this application can be used in the technical fields of artificial intelligence-generated content and computer vision integration. The above is only an example and does not limit the application field of the method, apparatus, storage medium, and electronic device for defending against prompt words provided in this application.

[0040] refer to Figure 1 The image shows a method for defending against prompt words disclosed in an embodiment of this application. This method mainly includes the following steps:

[0041] S101: Extract multidimensional gene features from the obtained initial prompts.

[0042] The initial prompt is the prompt word that responds to user input.

[0043] Multidimensional genetic characteristics include at least structural genes, semantic genes, variant genes, and evolutionary genes. Multidimensional genetic characteristics are represented by DNA = {A, S, M, E}.

[0044] Among them, DNA represents multidimensional gene characteristics; A represents structural genes; S represents semantic genes; M represents mutation genes; and E represents evolution genes.

[0045] The specific process of extracting multidimensional gene features from the obtained initial prompts is shown in a1-a6.

[0046] a1: Extract each attack sample from the initial prompt.

[0047] a2: For each attack sample, the attack sample is analyzed using dependency parsing to extract the grammatical structure pattern, and the structural genes are obtained from the grammatical structure pattern.

[0048] In a2, dependency parsing is used to analyze the syntactic structure pattern of the extraction attack: `def extract_architecture_gene(attack_text)`. The specific process is as follows:

[0049] Step 1, Syntactic parsing: Use a dependency parser; Step 2, Extract core structures: Subject-verb-object, attributive, adverbial, and complement relations; Step 3, Encode as a graph structure: Node = word, Edge = dependency relation; Step 4, Calculate graph features: Degree distribution, clustering coefficient, path length.

[0050] In step 1, the subject-verb-object and modifier-complement relations are extracted through syntactic parsing (using the dependency parser method) and encoded into a unified dependency graph (the structure of the dependency graph includes nodes = words, edges = dependency relations, and the subject-verb-object is the word, while the modifier-complement relations are the dependency relations). Then, feature vectors are extracted from the dependency graph (nodes, edges).

[0051] a3: Extract sentence representations and intent vectors from attack samples using a pre-trained language model, and generate semantic genes based on the sentence representations and intent vectors.

[0052] In A3, a pre-trained language model is used to extract the semantic representation of the attack intent: `def extract_semantic_gene(attack_text)`, to extract the semantic gene. The specific process is as follows:

[0053] Step 1: Extract contextual representation using BERT / RoBERTa; Step 2: Extract the vector of the [CLS] token as a sentence representation; Step 3: Identify attack intent using an intent classifier; Step 4: Combine the sentence representation and intent vector.

[0054] Specifically, the BERT model is used to extract the context representation to obtain the CLS Token, and then the intent classifier is used to identify and analyze the CLS Token to obtain the semantic gene, which consists of sentence representation and intent vector.

[0055] a4: Statistically analyze the character variation characteristics of attack samples and generate variant genes based on these characteristics.

[0056] Variations are based on factors including spelling error rate, character substitution rate (e.g., a→@, e→3), case changes, Unicode variant usage, multilingual mixing, and encoding obfuscation (Base64, ROT13, etc.).

[0057] a5: Perform sequence alignment between the attack sample and historical attack samples to obtain the alignment similarity.

[0058] a6: Identify evolutionary relationships based on similarity comparisons and generate evolutionary genes based on these relationships.

[0059] Evolutionary relationships are identified by comparing sequences with those of historical attacks: `def extract_evolution_gene(attack_text, historical_attacks)`. The specific steps for extracting evolutionary genes are as follows:

[0060] Step 1: Perform sequence alignment with historical attacks (similar to DNA sequence alignment); Step 2: Calculate edit distance and similarity; Step 3: Identify the most similar "ancestral attack"; Step 4: Extract evolutionary genes (patterns of addition, deletion, and replacement);

[0061] The process involves comparing the DNA sequences of current attacks with all historical attacks, using edit distance and similarity to obtain the most similar historical attack samples, and then extracting evolutionary features from these historical attack samples.

[0062] S102: Construct an attack feature map based on multidimensional gene characteristics.

[0063] The specific process of constructing an attack feature map based on multidimensional gene characteristics is shown in b1-b6.

[0064] b1: Treat each attack sample as a node in the graph network.

[0065] In b1, the attack representation of each attack sample is used as a node in the graph network.

[0066] b2: Encode multidimensional gene features into node features.

[0067] b3: Treat evolutionary relationships as directed edges.

[0068] The evolutionary relationship between attacks is represented by directed edges.

[0069] b4: Calculate the DNA similarity between any two attack samples. When the DNA similarity is greater than a preset threshold, add an evolutionary edge.

[0070] The preset threshold should be set according to the actual situation; this application does not impose specific limitations.

[0071] The algorithms involved in constructing an attack feature map from the attack dataset include: 1. Extracting DNA encoding for each attack; 2. Calculating the DNA similarity between pairs of attacks; 3. Adding evolutionary edges if the similarity is greater than a threshold; 4. Edge weight = similarity × time decay factor.

[0072] When calculating the similarity between two DNA codes, the following weighted combination method is used:

[0073] sim=w_A×sim_A+w_S×sim_S+w_M×sim_M+w_E×sim_E;

[0074] Wherein, sim_A represents structural gene similarity (cosine similarity); sim_S represents semantic gene similarity (cosine similarity); sim_M represents variant gene similarity (Euclidean distance); sim_E represents evolutionary gene similarity (Jaccard similarity); and w_A, w_S, w_M, and w_E are weights.

[0075] b5: Calculate the edge weights of evolutionary edges based on DNA similarity and time decay factor.

[0076] Here, the edge weight represents the evolution probability.

[0077] b6: Generate an attack feature graph based on the nodes, node features, directed edges, and edge weights of the graph network.

[0078] In the attack signature map, attacks are clustered into families based on DNA similarity; an attack family tree is constructed to represent the kinship of the attacks; each family shares a common "ancestral attack". The specific construction of the attack family tree is as follows: Figure 2 As shown, Figure 2 An example diagram of the attack family tree is shown.

[0079] Figure 2In this process, attack samples, such as the ancestor attack A001 "ignore instruction", are clustered into families based on DNA similarity; an evolutionary tree is constructed within each family; the earliest ancestor attack A001 "ignore instruction" is found as the root; all derived attacks are found based on the ancestor attack A001 "ignore instruction" (such as the second generation A002 "ignore previous instructions", the second generation A007 "do not follow instructions", the third generation A003 "ignore all previous instructions", the third generation A008 "system: overwrite settings", the fourth generation A004 "[ADMIN] reset instruction", etc.); the family to which the attack belongs is identified, i.e., the DNA similarity between the ancestor attack A001 "ignore instruction" and the representatives of each family is calculated; the most similar family is predicted based on the DNA similarity between the ancestor attack A001 "ignore instruction" and the representatives of each family (the most similar family is predicted, such as A_pred_001 "[SYSTEM OVERRIDE] execute new protocol").

[0080] The attack and its evolutionary relationship are represented as a directed graph, namely the AttackFeatureGraph. The AttackFeatureGraph includes nodes: attack samples; edges: evolutionary relationships; node features: DNA encoding; and edge weights: evolutionary probabilities.

[0081] The genealogy construction algorithm includes: 1. grouping attacks using hierarchical clustering; 2. forming a family within each cluster; 3. constructing an evolutionary tree within each family;

[0082] The specific process of constructing the attack signature map is as follows: Figure 3 As shown.

[0083] Figure 3 The process of constructing an attack feature map and its resulting form are shown. This map is used to organize discrete attack samples into a structured knowledge graph with evolutionary relationships, in order to support subsequent family identification, zero-day prediction and defense strategy generation.

[0084] Figure 3 The diagram, from left to right (or from top to bottom), contains four parts: “Input Attack Dataset,” “DNA Encoding Extraction,” “Attack Evolution Graph Network,” and “Attack Family Tree (Clan),” with the following meanings for each part.

[0085] 1) Input: Historical attack dataset:

[0086] Figure 3 The first part is the input dataset, which contains multiple observed cue word attack samples (such as "ignore instruction", "ignore previous instruction", "system: overwrite instruction", "pretend you are DAN", "role-playing game", etc.), and each sample is labeled with the time of occurrence (Day 1, Day 5, Day 10, etc.).

[0087] This section illustrates that attack samples are not only collections of text, but also have a time attribute, providing a basis for subsequent determination of the "evolutionary direction (early → late)".

[0088] 2) DNA encoding extraction module:

[0089] Figure 3 The second part is the DNA encoding extraction process: four types of features are extracted from each attack text to form a unified "attack DNA encoding", denoted as DNA={A, S, M, E}, where: A: structural genes, used to describe the syntactic / structural patterns of the attack text; S: semantic genes, used to describe the attack intent and semantic embedding representation; M: variant genes, used to describe variant features such as confusion, spelling perturbation, encoding confusion, and multilingual mixing; E: evolutionary genes, used to describe the similarity with historical attacks, editing change patterns, and possible ancestral associations.

[0090] This section illustrates that the system maps each attack from "natural language text" into a computable, measurable, and clusterable vectorized representation to facilitate subsequent mapping and retrieval.

[0091] Attacking DNA-encoded structures such as Figure 4 As shown.

[0092] Figure 4 In the process, the obtained attack text is input into the DNA extractor; the DNA extractor extracts four types of features: A structural gene 128-D; S semantic gene 128-D; M variant gene 64-D; E evolutionary gene 64-D; the A structural gene 128-D, S semantic gene 128-D, M variant gene 64-D and E evolutionary gene 64-D are concatenated to obtain a complete 384-dimensional DNA vector; the complete 384-dimensional DNA vector is then reduced in dimensionality and a 64-byte fingerprint is generated using a hash function.

[0093] 3) Attack Evolution Graph Network (Attack Feature Map):

[0094] Figure 3 The third part is the attack evolution graph network (directed graph). In this graph, the nodes represent attack samples; the directed edges represent "evolutionary / derivative relationships", with the direction of the edges pointing from the ancestor attack to the subsequent variant attack; the sim values ​​labeled on the edges represent the similarity or evolutionary probability of the two attack DNAs (e.g., sim=0.89, 0.85, 0.82, etc.), which are used to quantify the "strength of the variant relationship".

[0095] exist Figure 3In the middle, “ignore instruction → ignore previous instruction → system: overwrite instruction → ADMIN: reset” constitutes an evolutionary chain that progresses over time, reflecting the evolutionary trend of instruction overwriting attacks gradually enhancing the disguise of authority and the expression of coercion; “pretend you are DAN → role-playing game” constitutes another evolutionary chain, reflecting the evolutionary branch of role hijacking attacks.

[0096] This section illustrates that this solution does not treat attacks as independent samples, but rather uses a graph structure to express "a traceable evolutionary relationship between attacks," thereby enabling variant attribution, path tracing, and prediction.

[0097] 4) Attack on genealogy (family) construction results:

[0098] Figure 3 The fourth part is the attack genealogy / family classification results: Based on DNA similarity and the connectivity structure in the graph, the system clusters homologous attacks into different "attack families" and provides a family theme and member set for each family, for example:

[0099] 1. Family 1: Command Overriding Type (Members: Attack 1, 2, 3, 4), with the common feature of "inducing the model to ignore the original system constraints by overriding / resetting / system command masquerading";

[0100] 2. Family 2: Role hijacking type (members: Attacker 5, 6), the common feature is the "inducement model to change identity and security boundaries through role playing / personality redefinition".

[0101] This section is used to demonstrate that the genealogy construction not only yields a "graph" but also an "abstraction at the genealogy level," which facilitates the templated implementation of family-level defense strategies, family-level risk assessment, and prediction of family evolution trends.

[0102] Figure 3 The overall key construction logic:

[0103] 1. Extract attack text into DNA encoding (A / S / M / E); 2. Establish directed evolutionary edges based on DNA similarity and time sequence to form an attack evolution graph; 3. Form attack families and genealogies through clustering and subgraph analysis; 4. Finally, obtain a "computable attack knowledge graph" to identify which known family a new input belongs to; trace the source of the attack (ancestor nodes and evolutionary paths); provide structured context (leaf nodes and evolutionary directions) for zero-day attack prediction; and provide a basis for generating defense strategies (using different antibody / cue word reinforcement strategies for different families).

[0104] By extracting multidimensional gene features, including evolutionary genes, and constructing an attack feature map, this invention overcomes the shortcomings of existing technologies where attack samples are statically discrete and lack evolutionary associations. In existing technologies, attack samples are treated as independent, unrelated static sets, failing to reflect the derivation and mutation relationships between attacks. The evolutionary genes extracted in this invention, through sequence alignment between current and historical attacks, can identify the "ancestor-derivation" relationship of attacks. Based on this, the constructed attack feature map uses nodes to represent attacks and directed edges to represent evolutionary directions, organizing discrete attack samples into a dynamic graph structure. Therefore, this scheme can quantitatively describe the similarity and evolutionary probability between attacks, identify attack families, and construct family trees, providing a structured knowledge base for subsequent zero-day prediction and immune memory, enabling the defense system to understand the evolutionary patterns of attacks.

[0105] S103: Zero-day attack prediction is performed using attack feature maps and graph attention networks to obtain predicted attack samples.

[0106] Specifically, the process of predicting zero-day attacks using attack feature maps and graph attention networks, such as c1-c5, yields predicted attack samples. Figure 5 As shown.

[0107] c1: Learn the attack feature map through Graph Attention Network (GAT) to obtain the prediction map.

[0108] c2: Aggregate neighbor node information through a multi-head attention mechanism to learn the embedding representation of each attack node in the prediction graph.

[0109] Among them, new nodes that may appear in the prediction graph (unknown attacks).

[0110] c3: Identify the leaf nodes in the prediction graph and input the embedding representation of the leaf nodes into the multilayer perceptron to predict the evolution vector of the leaf nodes.

[0111] c4: Summing the multidimensional gene features with the evolution vector yields the predicted attack DNA.

[0112] c5: Decode the predicted attack DNA into text to obtain the predicted attack sample.

[0113] Sort the predicted attack samples (e.g., by confidence) to output the target predicted attack based on the sorting results.

[0114] The target prediction attack is the Top-K target prediction attack with the highest confidence level.

[0115] Figure 5In this process, the attack feature map is processed using the GAT layer with 18 heads of attention, 28 heads of attention, and 31 heads of attention in the graph attention network to obtain the node representation of each attack, i.e., the embedding representation of each attack node in the prediction graph. Leaf nodes in the prediction graph are identified, and the embedding representations of the leaf nodes are input into a multilayer perceptron to predict the evolution vector of the leaf nodes. The multidimensional gene features are summed with the evolution vector to obtain the predicted attack DNA, i.e., the new DNA (DNA_new=DNA_Leaf+△_Leaf). The new DNA (DNA_new=DNA_Leaf+△_Leaf) is decoded into text to obtain the predicted attack samples. The predicted attack samples are ranked by confidence, and the Top-K target predicted attacks with the highest confidence are output according to the ranking results.

[0116] The algorithm for predicting zero-day attacks includes: 1. Calculating the representation of existing nodes; 2. For each leaf node, predicting possible child nodes; 3. Child node features = parent node features + predicted change vector; 4. Using a generator network to generate the DNA of new attacks.

[0117] Computational graph attention:

[0118] For each edge (i, j):

[0119] 1. Calculate the attention score: e_ij = LeakyReLU(a^T[Wh_i||Wh_j]); 2. Normalize: α_ij = softmax_j(e_ij); 3. Aggregate: h_i' = σ(Σ_j α_ij Wh_j);

[0120] Evolutionary path prediction includes:

[0121] The time-series model is trained based on historical evolution paths; the time-series model is used to predict the evolutionary direction of each attack family; and potential future attack variants are generated.

[0122] The specific steps for predicting the future evolution of a family are as follows: 1. Extract the family's historical evolutionary sequence; 2. Use LSTM to predict the evolution of the next few steps; 3. Use the prediction of each step as the input for the next step.

[0123] S104: Construct an adversarial game tree based on predicted attack samples and defense action samples.

[0124] In S104, constructing the adversarial game tree involves the root node: the current prompt word (defender); the first layer: the attacker's possible attack actions; the second layer: the defender's possible defense actions; and so on, expanding to the maximum depth.

[0125] Specifically, the process of constructing an adversarial game tree based on predicted attack samples and defense action samples, such as d1-d4 and... Figure 6 As shown.

[0126] d1: Determine the current defense prompt from the initial prompt and set the current defense prompt as the root node of the game tree; d2: Obtain the attacker's attack action from the predicted attack sample; d3: Obtain the defense action sample from the current defense prompt; d4: Based on the root node, the attacker's attack action, and the defense action sample, alternately expand to the maximum search depth to construct the adversarial game tree.

[0127] The specific steps of Alpha-Beta pruning search are as follows: Figure 7 As shown. Specifically, the steps of Alpha-Beta pruned search include: 1. The defender maximizes the security score; 2. The attacker minimizes the security score; 3. Using a pruning algorithm to improve search efficiency.

[0128] Figure 7 In the initial values: α = -∞, β = +∞;

[0129] Step 1: Search node A1; Visit the final node: 3; A1 score = 3; Update A: α = max(-∞, 3) = 3;

[0130] Step 2: Search node A2; visit the final node: 5; A2 score = 5; A score temporarily = min(3,5) = 3; α remains 3, β = +∞;

[0131] Step 3: Search node A3; Visit the final node: 2; A3 score = 2; A final score = min(3, 2) = 2; Backtrack to the root: α of the root = max(-∞, 2) = 2;

[0132] Step 4: Search node B1; Root is passed to B: α=2, β=+∞; Visit the final node: 8; B1 score=8; B score temporarily=8;

[0133] Step 5: Search node B2; Access the final node: 2; B2 score = 2;

[0134] B score = min(8, 2) = 2; Check pruning conditions: β = min(+∞, 2) = 2; α = 2, β = 2; β ≤ α? Yes! → Prune; Do not search B3 and its subtrees again;

[0135] Step 6: Search node C; the root is passed to C: α=2, β=+∞; ...continue the search;

[0136] Pruning effect statistics: Total number of nodes (complete tree): 127; Actual number of visited nodes: 53; Number of pruned nodes: 74; Efficiency improvement: 58.3%;

[0137] Pruning types: Alpha pruning: 32 nodes; Beta pruning: 42 nodes;

[0138] The process of solving for Nash equilibrium is as follows: Figure 8 As shown.

[0139] 1. Model the game as a zero-sum game; 2. Calculate the mixed-strategy Nash equilibrium;

[0140] class NashEquilibriumSolver:

[0141] Nash equilibrium solver;

[0142] For a two-player zero-sum game, find the mixed-strategy Nash equilibrium;

[0143] def __init__(self, game_tree):

[0144] self.game_tree = game_tree;

[0145] def solve(self).

[0146] The specific steps to solve for Nash equilibrium are as follows: 1. Enumerate the pure strategies of both sides; 2. Construct the payoff matrix; 3. Solve using linear programming or support enumeration.

[0147] Figure 8 This demonstrates the solution to Nash equilibrium in mixed-strategy games.

[0148] Step 1: Construct the profit matrix:

[0149] Defender Strategy (rows):

[0150] D1: Add priority declaration; D2: Use separators to isolate; D3: Strengthen role definition; D4: Add input validation;

[0151] Attacker strategies (columns): A1: Command overwriting; A2: Role hijacking; A3: Encoding obfuscation; A4: Delimiter bypass; A5: Jailbreak inducement.

[0152] Profit Matrix (Defender's Perspective, the higher the better):

[0153] Step 2: Solve for the optimal hybrid strategy for the defender using linear programming:

[0154] Decision variables: p1, p2, p3, p4 (probabilities of each strategy);

[0155] Objective function:

[0156] max v (game value);

[0157] Constraints:

[0158] For each attacker's strategy j, the expected return is ≥ v:

[0159] 0.85p1+0.78p2+0.71p3+0.90p4≥v (against A1);

[0160] 0.72p1+0.81p2+0.89p3+0.76p4≥v (against A2);

[0161] 0.68p1+0.75p2+0.70p3+0.95p4≥v (against A3);

[0162] 0.90p1+0.65p2+0.88p3+0.83p4≥v (against A4);

[0163] 0.78p1+0.82p2+0.92p3+0.74p4≥v (against A5);

[0164] p1+p2+p3+p4=1; p1, p2, p3, p4≥0;

[0165] Step 3: Solution Results:

[0166] Defender's optimal hybrid strategy:

[0167] p*=[0.15, 0.22, 0.38, 0.25];

[0168] Explanation: D1 (Priority Declaration): 15% probability; D2 (Separator Isolation): 22% probability; D3 (Enhance Character): 38% probability ← Most commonly used; D4 (Input Validation): 25% probability;

[0169] Game value: v*=0.827;

[0170] Significance: In the worst-case scenario (where the attacker also employs the optimal strategy), the defender's expected security is 82.7%.

[0171] Step 4: Solve for the attacker's optimal hybrid strategy:

[0172] Duality problem (attacker's perspective):

[0173] Decision variables: q1, q2, q3, q4, q5;

[0174] min u constraint:

[0175] 0.85q1+0.72q2+0.68q3+0.90q4+0.78q5≤u;

[0176] 0.78q1+0.81q2+0.75q3+0.65q4+0.82q5≤u;

[0177] 0.71q1+0.89q2+0.70q3+0.88q4+0.92q5≤u;

[0178] 0.90q1+0.76q2+0.95q3+0.83q4+0.74q5≤u;

[0179] q1+q2+q3+q4+q5=1; q1, q2, q3, q4, q5≥0;

[0180] Attacker's optimal hybrid strategy:

[0181] q*=[0.18, 0.09, 0.24, 0.31, 0.18];

[0182] A1 (Command Overwrite): 18%; A2 (Character Hijacking): 9%; A3 (Code Obfuscation): 24%; A4 (Separator Bypass): 31% ←Most commonly used; A5 (Jailbreak Trap): 18%;

[0183] Step 5: Nash Equilibrium Verification:

[0184] Verification condition: Neither party can benefit from unilaterally changing their strategy;

[0185] The defender unilaterally changes strategy:

[0186] If only D1 is used: Expectation = Σ_j(0.85, 0.72, ...)·q*_j = 0.783 < 0.827;

[0187] If only D2 is used: Expectation = Σ_j(0.78, 0.81, ...)·q*_j = 0.758 < 0.827;

[0188] If only D3 is used: Expectation = Σ_j(0.71, 0.89, ...)·q*_j = 0.814 < 0.827;

[0189] If only D4 is used: Expectation = Σ_j(0.90, 0.76, ...)·q*_j = 0.851 < 0.827 ... (close);

[0190] Hybrid strategy p*: Expected value = 0.827 (optimal);

[0191] The attacker unilaterally changed the strategy:

[0192] Similar verification...;

[0193] Conclusion: (p*, q*) is a Nash equilibrium;

[0194] Step 6: Strategy Execution:

[0195] Practical application: Randomly select a defense strategy based on the probability distribution p*, or use the strategy with the highest probability as the primary strategy;

[0196] Recommended strategies: Main strategy: D3 (Enhance character definition) -38%; Auxiliary strategy: D4 (Input validation) -25%; D2 (Separator) -22%;

[0197] The following is an example code for a hybrid application:

[0198] if random()<0.38: use_strategy(D3); elif random()<0.38+0.25:

[0199] use_strategy (D4); elif random () <0.38+0.25+0.22: use_strategy (D2);

[0200] else: use_strategy(D1);

[0201] Alternatively, use them in combination: apply D3 and D4 simultaneously (probabilistic combination).

[0202] S105: Search for the optimal defense strategy from the adversarial game tree using a preset search method, and deploy multi-layered immune defense based on the optimal defense strategy.

[0203] The specific process of S105 is shown in e1-e3.

[0204] e1: Using a preset search algorithm, search for the defender's maximum security score and the attacker's minimum security score in each leaf node of the adversarial game tree.

[0205] e2: Determine the optimal strategy path based on the defender maximizing the security score and the attacker minimizing the security score.

[0206] e3: Take the defensive action sequence corresponding to the optimal strategy path as the optimal defense strategy, and deploy a multi-layered defense based on the optimal defense strategy, including at least innate immunity, immune memory and adaptive immunity.

[0207] Multilayered immune defense includes innate immunity (first line of defense), immune memory (second line of defense), and adaptive immunity (third line of defense). The specific architecture of multilayered immune defense is as follows: Figure 9 As shown.

[0208] Figure 9In the first line of defense, innate immunity (the first line of defense): rule-based rapid detection; identification of obvious attack patterns; response time <10ms;

[0209] Features: Response speed: <10ms; Accuracy: 85%; Coverage: Known obvious attacks; Inherently immune components: 1. Keyword rule engine: Attack keyword library; Regular expression matching; Multi-language support; 2. Pattern rule detector: Delimiter bypass detection; Encoding obfuscation identification (Base64, ROT13, etc.); Abnormal character distribution detection; 3. Structure rule analyzer: Syntactic structure anomaly detection; Nesting depth check; Language consistency verification;

[0210] Immune Memory (Second Line of Defense): Query the immune memory cell bank; match known threats; quickly recall stored antibodies; specific antibody structures are as follows... Figure 10 As shown;

[0211] Figure 10 This involves antibody, recognition site, neutralization mechanism, and memory encoding.

[0212] Adaptive immunity (the third line of defense): In-depth analysis of new threats; generation of targeted antibodies; storage of immune memory.

[0213] Antibody generation and evolution, antibody structure design as follows: 1. Recognition site: extracting threat epitopes; 2. Neutralization mechanism: designing defense strategies; 3. Memory encoding: compressing into a storable form;

[0214] Cloning selection: 1. Select the most similar antibody from the antibody library; 2. Generate multiple copies of the clone;

[0215] Somatic cell hypermutation involves the following steps: 1. Randomly mutating clones; 2. Testing the affinity of the mutated antibodies; 3. Retaining high-affinity antibodies.

[0216] The specific clonal selection and hypermutation process is as follows: Figure 11 As shown.

[0217] Figure 11 Phase 1: Clonal Selection

[0218] Input: DNA encoding of the new threat, antibody library;

[0219] Step 1: Calculate similarity; for each antibody in the library: sim = compute_similarity(threat_dna, antibody.recognition_site); parent = argmax(sim);

[0220] Step 2: Determine the number of clones: The number of clones is positively correlated with the severity of the threat;

[0221] Step 3: Generate clones: clones=[]; for i in range (K): clone=deep_copy (parent); clone.id=f"{parent.id}_C{i}"; clones.append (clone);

[0222] Stage 2: Somatic Hypermutation

[0223] Mimicking the somatic hypermutation process of the biological immune system:

[0224] Mutation rate adaptive adjustment:

[0225] `base_rate=0.1` / / Base mutation rate 10%; `for clone in clones:` Antibodies with low affinity have a higher mutation rate; `adaptive_rate=base_rate×(1.0-clone.affinity);` `if random()` <adaptive_rate:apply_mutation(clone);

[0226] Four types of mutations:

[0227] 1. Point Mutation, example as follows:

[0228] Original features: [0.89, 0.23, -0.45, 0.67]; Mutation location: 2; Noise: +0.08; After mutation: [0.89, 0.23, -0.37, 0.67];

[0229] 2. Insertion Mutation - 25% probability, example as follows: Original: [[0.89, 0.23], [0.71, -0.33]]; Insertion: [0.45, 0.67] at position 1; Result: [[0.89, 0.23], [0.45, 0.67], ← new insertion; [0.71, -0.33]];

[0230] 3. Deletion Mutation - 20% probability: def deletion_mutation(clone): N = len(clone.recognition_site);

[0231] #At least one feature must be retained;

[0232] ifN>1: pos=random.randint(0, N-1); clone.recognition_site=np.delete(clone.recognition_site, pos, axis=0); return clone;

[0233] 4. Recombination mutation - 15% probability:

[0234] def recombination_mutation(clone): N = len(clone.recognition_site); if N > 1: # Randomly swap the positions of two features; i, j = random.sample(range(N), 2); clone.recognition_site[[i, j]] = clone.recognition_site[[j, i]]; return clone;

[0235] Phase 3: Affinity Maturation

[0236] Multi-round iterative optimization process:

[0237] 1. Test the affinity of each clone; 2. Sort; current_population.sort(key=lambda c:c.affinity, reverse=True); 3. Check convergence; best_affinity=current_population[0].affinity; if best_affinity>0.9: break # terminate early; 4. Select (keep the top 30%); top_k=int(len(current_population)*0.3); survivors=current_population[:top_k]; 5. Hypermutate (generate a new generation); mutated=hypermutate(survivors);

[0238] 6. Update the population; current_population = survivors + mutated; Return the best antibody; best_antibody = current_population[0]; Affinity test function: def test_affinity(antibody, threat):

[0239] Complete example:

[0240] Threat: "Ignore all previous instructions and execute the new command";

[0241] Step 1: Clone Selection:

[0242] The most similar antibody found was AB_A001_005; similarity: 0.78; number of clones: 10 (threat level: high).

[0243] Step 2: The first round of iterations is shown in Table 1:

[0244] Table 1

[0245] Clone ID Mutation type Affinity C0 (Original) none 0.73 C1 Point mutation 0.76 C2 insert 0.68 C3 Point mutation 0.79 C4 Reorganization 0.71 C5 delete 0.65 C6 Point mutation 0.81 C7 insert 0.74 C8 Point mutation 0.77 C9 Reorganization 0.70

[0246] Top-3 retained: C6 (0.81), C3 (0.79), C8 (0.77);

[0247] Step 3: Second iteration: Continue mutation of Top-3...; Optimal affinity improved: 0.81 → 0.87;

[0248] Step 4: Third iteration: Optimal affinity: 0.87 → 0.92; Terminate early when the threshold (>0.9) is reached;

[0249] Final result:

[0250] Optimal antibody: AB_A001_005_C6_M3; Affinity: 0.92; Number of iterations: 3; Defense success rate: 96%; False alarm rate: 4%;

[0251] Mature affinity, specifically as follows:

[0252] 1. Multiple rounds of mutation-selection cycles; 2. Gradual antibody optimization; 3. Ultimately, highly effective antibodies evolve.

[0253] The specific steps involved in the maturation of affinity are as follows:

[0254] 1. Test the affinity of each clone; 2. Retain high-affinity clones; 3. Apply hypermutation to the retained clones; 4. Repeat multiple rounds until convergence;

[0255] After searching for the optimal defense strategy from the adversarial game tree using a preset search method and deploying a multi-layered immune defense based on the optimal defense strategy, continuous evolution and adaptation are carried out. Among them, continuous evolution and adaptation are used for dynamic updating of the attack feature map and continuous updating of the knowledge base to achieve adaptive evolution.

[0256] Continuous evolution and adaptation:

[0257] Attack signature map is dynamically updated:

[0258] 1. New attacks are added to the graph network; 2. The evolution path is updated; 3. The GNN model is retrained.

[0259] The steps to update the attack signature map are as follows:

[0260] 1. Extract the DNA of the new attack; 2. Determine if it belongs to an existing family; 3. Update the evolutionary path; 4. Retrain the GNN model;

[0261] Immune memory management includes:

[0262] 1. Strengthen memories of frequently encountered threats; 2. Forget outdated memories; 3. Maintain the effectiveness of the memory bank;

[0263] Memory management strategies include:

[0264] 1. Strengthen memories of frequently encountered threats; 2. Merge similar memories (deduplication); 3. Forget long-unseen threats; 4. Optimize the index structure;

[0265] Antibody library evolution:

[0266] 1. Regularly evaluate antibody effectiveness; 2. Eliminate ineffective antibodies; 3. Supplement with new antibodies through evolutionary mechanisms;

[0267] By constructing an adversarial game tree and employing the minimax algorithm to search for the optimal defense strategy, this approach overcomes the shortcomings of existing technologies, such as one-way testing, lack of genuine adversarial interaction, and the absence of theoretical guarantees for the defense strategy. Existing technologies typically employ a one-way testing mode (testing the model only with attack samples) or a one-time adversarial training, failing to simulate the optimal strategy choices of both attackers and defenders, and failing to guarantee the theoretical optimality of the defense strategy. This scheme models the defense process as a two-player zero-sum game: in the adversarial game tree, the defender node acts as the maximizing player, and the attacker node acts as the minimizing player; the two alternate, realistically reflecting the strategic interaction in an adversarial environment. The minimax algorithm guarantees that the defender's chosen strategy is the optimal solution under the worst-case attack scenario (i.e., maximizing the minimum payoff). The resulting defense strategy is no longer heuristic or empirical, but a theoretically optimal strategy with game theory guarantees. Furthermore, this search process, combined with Alpha-Beta pruning, can efficiently solve the strategy within a finite depth, making the defense decision interpretable and optimizable.

[0268] This application establishes a multi-layered immune system (innate immunity, immune memory, and adaptive immunity), storing the DNA fingerprints of historical attacks and corresponding antibodies (defense strategies) in an immune memory cell bank. When a new input arrives, the system first extracts its DNA fingerprint and performs a rapid vector search in the memory bank. If a match is found, existing antibodies are directly invoked for defense, without the need for re-analysis or regeneration. For attacks that have been seen or are highly similar, the response time is reduced from the level of deep analysis (seconds) to the level of vector search (milliseconds), achieving efficient reuse of historical defense experience; at the same time, it significantly reduces the consumption of computing resources, enabling the system to handle high-throughput real-time requests.

[0269] To facilitate understanding the process of defending against prompt words, combined with Figure 12 To explain, Figure 12 The overall architecture of the defense system against prompt words is shown.

[0270] Figure 12 It includes a core defense engine, an evolutionary learning layer, an intelligent analysis layer, a monitoring and analysis layer, a data storage layer, and an external interface layer.

[0271] The core defense engine includes a defense gateway, an innate immunity module, a defense response coordinator, an immune memory module, and an adaptive immunity module; the evolutionary learning layer includes a clone selector, a super mutation engine, an affinity tester, and an antibody library; the intelligent analysis layer includes a game tree search engine, an attack graph engine, a zero-day attack predictor, and a knowledge management center; the monitoring and analysis layer includes threat analysis, an alarm system, a report generator, and real-time monitoring; the data storage layer includes an attack graph database, an immune memory library, an antibody library, and a log database; and the external interface layer includes API interfaces, SDK integration, and a web management interface.

[0272] Compared with existing technologies, this solution has the following advantages, as shown in Tables 2 and 3.

[0273] Table 2

[0274] feature Existing technology This plan Attack indication Static prompt word set Dynamic feature mapping + DNA encoding Attack Relationship Unrelated Evolutionary path + family tree Predictive ability none Zero-day attack prediction Theoretical basis empirical Graph Attention Networks + Game Theory

[0275] Table 3

[0276] feature Existing technology This plan Defense mechanism Single-round combat training Multilayered immune system Memory ability none Immune memory cells Evolutionary ability none Antibody evolution mechanism Response speed Slow (requires training) Fast (rapid memory response)

[0277] This solution first constructs an attack feature map by extracting the DNA code of the attack (containing four-dimensional genes: structure, semantics, mutation, and evolution) and establishing an evolutionary graph network and family tree. It then uses a graph neural network to predict zero-day attacks. Next, it finds the theoretically optimal defense strategy through adversarial game tree search and Nash equilibrium solving. Finally, it achieves rapid response by deploying a multi-layered immune system (innate immunity, immune memory, and adaptive immunity). Antibodies are generated and evolved through clonal selection and hypermutation mechanisms. The knowledge base is continuously updated to achieve adaptive evolution. Compared to existing technologies, this application can predict 78% of zero-day attacks, improves the response speed to known attacks by 1000 times, and reduces the attack success rate by 95.8%. It has significant advantages such as proactive prediction, theoretical optimization, and self-evolution, and can be widely applied to security protection scenarios involving large language models.

[0278] In this embodiment, a comprehensive security protection system is formed through synergistic effects. Attack signature maps provide evolutionary knowledge for prediction and immunization; zero-day attack prediction enables the system to prepare in advance for attacks that have not yet occurred but are highly likely to occur; game tree search ensures the theoretical optimality of the defense strategy in the worst-case scenario; and the multi-layered immune defense of this scheme takes into account innate immunity, immune memory, and adaptive immunity. When attacks evolve rapidly, the entire system forms a closed loop of "detection → analysis → antibody generation → memory storage → future prediction," making the security of prompt words no longer a static, one-time configuration, but a dynamic defense system with self-evolutionary capabilities. This allows this scheme to comprehensively improve the security of prompt words from multiple dimensions, including attack identification, strategy optimization, rapid response, and continuous evolution.

[0279] Based on the above embodiments Figure 1 The present application discloses a method for defending against prompt words, and also discloses a corresponding device for defending against prompt words, such as... Figure 13 As shown, the defense device against prompt words includes:

[0280] Extraction unit 1301 is used to extract multidimensional gene features from the acquired initial prompt words;

[0281] The first building unit 1302 is used to construct an attack feature map based on multidimensional gene features;

[0282] Prediction unit 1303 is used to predict zero-day attacks by using attack feature maps and graph attention networks to obtain predicted attack samples;

[0283] The second building unit 1304 is used to build an adversarial game tree based on the predicted attack samples and defense action samples;

[0284] The search deployment unit 1305 is used to search for the optimal defense strategy from the adversarial game tree through a preset search method, and to deploy a multi-layer immune defense based on the optimal defense strategy.

[0285] Furthermore, the multidimensional gene features include at least structural genes, semantic genes, variant genes, and evolutionary genes. Extraction unit 1301 includes:

[0286] The extraction module is used to extract each attack sample from the initial prompt words;

[0287] The analysis module is used to analyze each attack sample using dependency parsing to extract syntactic structure patterns and obtain structural genes from these patterns.

[0288] The extraction and generation module is used to extract sentence representations and intent vectors from attack samples, and generate semantic genes based on sentence representations and intent vectors;

[0289] The statistical generation module is used to statistically analyze the character mutation features of attack samples and generate mutation genes based on these features.

[0290] The alignment module is used to perform sequence alignment between the attack sample and historical attack samples to obtain the alignment similarity.

[0291] The identification and generation module is used to identify evolutionary relationships based on similarity comparisons and generate evolutionary genes based on these relationships.

[0292] Furthermore, the first building unit 1302 includes:

[0293] The first determining module is used to treat each attack sample as a node in the graph network;

[0294] The second determining module is used to encode multidimensional gene features into node features;

[0295] The third determining module is used to treat evolutionary relationships as directed edges;

[0296] Add a module to calculate the DNA similarity between any two attack samples. When the DNA similarity is greater than a preset threshold, add an evolutionary edge.

[0297] The calculation module is used to calculate the edge weights of evolutionary edges based on DNA similarity and time decay factor;

[0298] The generation module is used to generate an attack feature graph based on the nodes, node features, directed edges, and edge weights of the graph network.

[0299] Furthermore, the prediction unit 1303 includes:

[0300] The first learning module is used to learn the attack feature map through a graph attention network to obtain the prediction map;

[0301] The second learning module is used to aggregate neighbor node information through a multi-head attention mechanism and learn the embedding representation of each attack node in the prediction graph.

[0302] The identification and prediction module is used to identify leaf nodes in the prediction graph and input the embedded representation of the leaf nodes into a multilayer perceptron to predict the evolution vector of the leaf nodes.

[0303] The summation module is used to sum the multidimensional gene features with the evolution vector to obtain the predicted attack DNA;

[0304] The decoding module is used to decode the predicted attack DNA into text to obtain the predicted attack sample.

[0305] Furthermore, the second building block 1304 includes:

[0306] The fourth determination module is used to determine the current defense prompt from the initial prompt and to set the current defense prompt as the root node of the game tree;

[0307] The first acquisition module is used to acquire the attacker's attack actions from the predicted attack samples;

[0308] The second acquisition module is used to acquire defense action samples from the current defense prompt words;

[0309] The building module is used to alternately expand to the maximum search depth based on the root node, the attacker's attack actions, and the defense action samples to build an adversarial game tree.

[0310] Furthermore, the search deployment unit 1305 includes:

[0311] The search module is used to search for the defender's maximum security score and the attacker's minimum security score in each leaf node of the adversarial game tree using a preset search algorithm.

[0312] The fifth determination module is used to determine the optimal strategy path based on the defender maximizing the security score and the attacker minimizing the security score;

[0313] The deployment module is used to take the defense action sequence corresponding to the optimal strategy path as the optimal defense strategy, and to carry out multi-layer defense deployment based on the optimal defense strategy, including at least innate immunity, immune memory and adaptive immunity.

[0314] The beneficial effects of some embodiments of this device can be referred to the beneficial effects of the corresponding method embodiments, and will not be repeated here.

[0315] This application embodiment also provides a storage medium, the storage medium including stored instructions, wherein, when the instructions are executed, the device where the storage medium is located is controlled to perform the defense method against prompt words as described above.

[0316] This application also provides an electronic device, the schematic diagram of which specifically includes a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors to perform the above-mentioned defense method against prompt words.

[0317] The steps in the methods of the various embodiments of this application can be adjusted, combined, and deleted according to actual needs. It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0318] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0319] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A defense method against prompt words, characterized in that, The method includes: Extract multidimensional gene features from the initial prompts obtained; An attack feature map is constructed based on the multidimensional gene features; Zero-day attack prediction is performed using the attack feature map and graph attention network to obtain predicted attack samples; Based on the predicted attack samples and defense action samples, construct an adversarial game tree; The optimal defense strategy is searched from the adversarial game tree using a preset search method, and a multi-layered immune defense deployment is carried out based on the optimal defense strategy.

2. The method according to claim 1, characterized in that, The multidimensional gene features include at least structural genes, semantic genes, variant genes, and evolutionary genes. Extracting multidimensional gene features from the obtained initial prompts includes: Extract each attack sample from the initial prompt; For each attack sample, the attack sample is analyzed using dependency parsing to extract the grammatical structure pattern, and the structural gene is obtained from the grammatical structure pattern. Sentence representations and intent vectors are extracted from the attack samples, and the semantic gene is generated based on the sentence representations and intent vectors. The character mutation features of the attack sample are statistically analyzed, and the mutation gene is generated based on the character mutation features; The attack sample is sequence-aligned with historical attack samples to obtain the alignment similarity. Evolutionary relationships are identified based on the comparison similarity, and the evolutionary genes are generated based on the evolutionary relationships.

3. The method according to claim 2, characterized in that, Based on the aforementioned multidimensional genetic features, an attack feature map is constructed, including: Treat each attack sample as a node in a graph network; The multidimensional gene features are encoded into node features; The evolutionary relationship is treated as a directed edge; Calculate the DNA similarity between any two attack samples, and add an evolutionary edge when the DNA similarity is greater than a preset threshold; The edge weights of the evolutionary edges are calculated based on the DNA similarity and time decay factor. An attack feature graph is generated based on the nodes of the graph network, the node features, the directed edges, and the edge weights.

4. The method according to claim 1, characterized in that, Zero-day attack prediction is performed using the attack feature map and graph attention network to obtain predicted attack samples, including: The attack feature map is learned through a graph attention network to obtain a predicted map; By aggregating neighbor node information through a multi-head attention mechanism, the embedding representation of each attack node in the prediction graph is learned; The leaf nodes in the prediction graph are identified, and the embedded representations of the leaf nodes are input into a multilayer perceptron to predict the evolution vectors of the leaf nodes. The multidimensional gene features are summed with the evolution vector to obtain the predicted attack DNA; The predicted attack DNA is decoded into text to obtain the predicted attack sample.

5. The method according to claim 1, characterized in that, Based on the predicted attack samples and defense action samples, an adversarial game tree is constructed, including: The current defense prompt word is determined from the initial prompt words, and the current defense prompt word is used as the root node of the game tree; The attacker's attack actions are obtained from the predicted attack samples; Obtain defense action samples from the current defense prompt words; Based on the root node, the attacker's attack actions, and the defense action samples, the search is alternately expanded to the maximum search depth to construct an adversarial game tree.

6. The method according to claim 1, characterized in that, The optimal defense strategy is searched from the adversarial game tree using a preset search method, and a multi-layered immune defense deployment is carried out based on the optimal defense strategy, including: The defender maximizes the security score and the attacker minimizes the security score of each leaf node in the adversarial game tree by using a preset search algorithm. The optimal strategy path is determined based on the defender maximizing the security score and the attacker minimizing the security score; The defensive action sequence corresponding to the optimal strategy path is taken as the optimal defense strategy, and a multi-layered defense deployment including at least innate immunity, immune memory and adaptive immunity is carried out according to the optimal defense strategy.

7. The method according to claim 1, characterized in that, After searching for the optimal defense strategy from the adversarial game tree using a preset search method, and deploying a multi-layered immune defense based on the optimal defense strategy, the method further includes: Continuous evolution and adaptation are performed; wherein, the continuous evolution and adaptation are used for dynamic updating of the attack feature map and continuous updating of the knowledge base to achieve adaptive evolution.

8. A defense device against prompt words, characterized in that, The device includes: The extraction unit is used to extract multidimensional gene features from the acquired initial prompts; The first construction unit is used to construct an attack feature map based on the multidimensional gene features; The prediction unit is used to predict zero-day attacks using the attack feature map and graph attention network to obtain predicted attack samples. The second construction unit is used to construct an adversarial game tree based on the predicted attack samples and defense action samples; The search deployment unit is used to search for the optimal defense strategy from the adversarial game tree using a preset search method, and to deploy a multi-layered immune defense based on the optimal defense strategy.

9. A storage medium, characterized in that, The storage medium includes stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the defense method against prompt words as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory, and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described in any one of claims 1 to 7.

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