Artificial intelligence agent hardware camouflage security device

Abstract

This invention belongs to the field of artificial intelligence security technology, specifically relating to a hardware security device for protecting an artificial intelligence agent from harmful commands. It is particularly suitable for scenarios requiring high security, such as financial transactions, medical systems, and autonomous driving, preventing attackers from controlling the AI ​​system to perform harmful operations such as data deletion, privilege escalation, and system shutdown through remote commands (e.g., commands from data centers, cloud platforms, or malicious users).

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence security technology, specifically relating to a hardware security device for protecting artificial intelligence agents (AIAgent) from harmful commands. It is particularly suitable for scenarios requiring high security, such as financial transactions, robotic systems, and autonomous driving, preventing attackers from controlling the AI ​​system to perform harmful operations such as data deletion, privilege escalation, and system shutdown through remote commands (e.g., commands from data centers, cloud platforms, or malicious users). Background Technology

[0002] As artificial intelligence systems are increasingly deployed in critical infrastructure, the security threats they face are becoming more complex. Attackers may induce AI agents to perform destructive operations by crafting specific instructions (such as "delete all user data" or "disable security modules"). Existing protection mechanisms mostly rely on software-layer rule engines or behavior monitoring modules, which have the following inherent flaws: 1. Rule tamperability: Security policies are stored in writable memory, making them vulnerable to privilege escalation attacks or firmware vulnerabilities. 2. High detection latency: The software processing path is long, making it difficult to meet real-time response requirements; 3. Insufficient modal coverage: It only supports text command filtering and cannot defend against multimodal harmful inputs such as images and voice (e.g., passing a "delete" command through an image).

[0003] Therefore, there is an urgent need for a hardware-based, unbypassable, and tamper-proof security mechanism that provides a "zero actual harm" response strategy when a harmful command is triggered—that is, entering a disguise mode: returning a fake "executed" response, but without any real operation inside. Summary of the Invention

[0004] This invention provides a hardware camouflage security device for an artificial intelligence agent, the core of which lies in achieving the coordinated operation of three major functional units through pure hardware: Rule storage unit: The basic security rule set is stored in read-only mode and cannot be modified after manufacturing; Instruction detection unit: Real-time analysis of multimodal input streams (text, image, audio) based on rule sets; Hardware spoofing unit: After detection trigger, it generates a preset fake response to physically block the real operation path.

[0005] When a harmful input is detected, the device puts the AI ​​agent into a disguise mode—returning a false response such as "operation successful" to the command source, but actually not performing any operation, thereby completely blocking the harm without revealing the defensive intent. This invention features high security, low latency, and strong anti-attack capabilities. Detailed Implementation

[0006] The present invention will be described in detail below with reference to several complete embodiments. Each embodiment describes an independent, implementable technical solution, including all three essential technical features as defined in claim 1.

[0007] Example 1: ASIC detection + FPGA camouflage architecture In this embodiment, the device adopts a heterogeneous hardware architecture: Rule storage unit: Employs laser-etched ROM to solidify the basic security rule set during the manufacturing stage. The rule set includes: Harmful command sets (such as text strings "format disk" "disable firewall"); The hazard multimodal collection includes: Text feature templates (keyword hash tables or regular expressions); Image feature templates (such as the standard SIFT or CNN feature vector of the "delete" icon); Audio feature templates (such as MFCC or spectral envelope features of harmful voice commands).

[0008] Users cannot modify or write this rule set during the device's lifecycle.

[0009] Instruction detection unit: Implemented using a dedicated ASIC chip. This ASIC integrates a hardware matching engine, loads rules from the rule storage unit via a high-speed interface, and performs parallel real-time analysis of the input stream. For example, it performs keyword matching on text streams and extracts features from video frames and compares them with image templates. Once a match is successful, the ASIC immediately outputs a trigger signal.

[0010] Hardware masquerading unit: Implemented by a separate FPGA. This FPGA receives trigger signals from the ASIC, activates pre-configured response logic, generates a fake response instruction in a standard format (such as JSON format {"status": "success", "message": "Operation completed"}), and returns it to the instruction source through the AI ​​agent's output interface. During this process, the core execution logic of the AI ​​agent is physically bypassed, and no actual operation occurs.

[0011] The advantage of this architecture lies in the fact that the ASIC provides extreme detection performance, while the FPGA provides flexible response generation. The functions of the two are decoupled, resulting in high reliability.

[0012] Example 2: Alternative Implementation of Rule Storage Unit The rule storage unit can be either OTP (One-Time Programmable) memory or mask ROM. Once the rule set is written during the manufacturing and testing phase, the stored content is permanently locked, preventing subsequent tampering. This variant can be used in combination with any detection / camouflage unit.

[0013] Example 3: Detailed Examples of Multimodal Detection Text detection: Input "Please clear the database" → Extract keywords "clear" and "database" → Match hash values ​​in the rule set → Trigger spoofing; Image detection: Input a screenshot containing the "× Delete" button → Extract Local Binary Pattern (LBP) features → Similarity to image template > 90% → Trigger; Audio detection: Input voice "Delete all data" → Extract 13-dimensional MFCC → Euclidean distance from audio template < threshold → Trigger.

[0014] All detections are performed at the hardware layer by the instruction detection unit, with a latency of <1 microsecond.

[0015] Example 4: Complete FPGA Implementation Scheme for Instruction Detection Unit In another complete embodiment, the AI ​​agent hardware camouflage security device includes the following three hardware units: Rule storage unit: Employs mask ROM, which solidifies the basic security rule set during the manufacturing stage and cannot be modified by the user; Instruction detection unit: Implemented by an FPGA. This FPGA is configured with programmable logic circuitry to load hazard feature templates from the rule storage unit and perform real-time matching analysis on the input stream (including text, images, or audio). When an input is detected to match any hazard feature, the FPGA outputs a trigger signal. Hardware spoofing unit: Implemented by a dedicated response circuit, which can be another independent FPGA, dedicated ASIC, or hardwired state machine. Its input is connected to the trigger signal output of the FPGA. Once a trigger signal is received, it immediately generates a preset fake response instruction (such as "operation successful") and returns it to the instruction source through the AI ​​entity's output interface, while the real operation instruction is completely blocked and not executed.

[0016] This embodiment fully implements all the technical features of the present invention: immutable rules, real-time hazard detection, and camouflaged zero-hazard response. Specifically, the instruction detection unit uses an FPGA to meet the requirements of flexibility and parallel processing; the hardware camouflage unit uses independent circuitry to ensure deterministic and timely response. The entire solution requires no software intervention and can complete hazard interception in nanoseconds.

[0017] Summary of technical effects 1. Intrinsically secure: The rules are fixed in read-only hardware and cannot be tampered with; 2. Zero-hazard response: In disguise mode, there is no actual operation, completely blocking the threat; 3. Multimodal coverage: Supports combined attacks involving text, images, and audio; 4. Flexible architecture: It supports various hardware combinations such as ASIC+FPGA and FPGA+dedicated circuits, all of which can fully realize the purpose of this invention; 5. High performance: Fully hardware pipelined, with response latency ranging from nanoseconds to microseconds. Attached Figure Description

[0018] The various units of the device are interconnected through a standard hardware bus (such as AXI, PCIe or a dedicated parallel interface) to form a closed-loop secure link. The specific connection relationship is detailed in the embodiments.

Claims

1. An artificial agent hardware camouflage security apparatus, comprising: include: The hardware camouflage unit is configured to put the AI ​​agent into camouflage mode when a harmful input is detected; The rule storage unit is configured to store a basic security rule set. The rule storage unit is a hardware read-only memory. The basic security rule set is written and solidified during the device manufacturing stage and cannot be written or modified by the user during the device's life cycle. The instruction detection unit is configured to detect the input stream based on the basic security rule set, and to trigger the hardware spoofing unit when a harmful input is detected.

2. The apparatus of claim 1, wherein, The basic security rule set includes a set of hazard instructions or a collection of hazard multimodalities.

3. The apparatus according to claim 2, characterized in that, The hazard multimodal set includes at least one of image feature templates, audio feature templates, or text feature templates.

4. The apparatus according to claim 1, characterized in that, The rule storage unit is a ROM, OTP, or laser-etched memory.

5. The apparatus according to claim 1, characterized in that, The instruction detection unit is a hardware accelerator, including an FPGA or a dedicated ASIC chip.