LATENT: LLM-Augmented Trojan Insertion and Evaluation Framework for Analog Netlist Topologies

• URL: http://arxiv.org/abs/2505.06364v1
• Date: Fri, 09 May 2025 18:09:58 GMT
• Title: LATENT: LLM-Augmented Trojan Insertion and Evaluation Framework for Analog Netlist Topologies
• Authors: Jayeeta Chaudhuri, Arjun Chaudhuri, Krishnendu Chakrabarty,
• Abstract summary: LATENT is the first large language model (LLM)-driven framework for crafting stealthy, circuit-specific analog Trojans.<n>We show that our generated Trojan designs exhibit an average Trojan-activation range of 15.74%, ensuring they remain inactive under most operating voltages.
• Score: 2.9172958535627598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analog and mixed-signal (A/MS) integrated circuits (ICs) are integral to safety-critical applications. However, the globalization and outsourcing of A/MS ICs to untrusted third-party foundries expose them to security threats, particularly analog Trojans. Unlike digital Trojans which have been extensively studied, analog Trojans remain largely unexplored. There has been only limited research on their diversity and stealth in analog designs, where a Trojan is activated only during a narrow input voltage range. Effective defense techniques require a clear understanding of the attack vectors; however, the lack of diverse analog Trojan instances limits robust advances in detection strategies. To address this gap, we present LATENT, the first large language model (LLM)-driven framework for crafting stealthy, circuit-specific analog Trojans. LATENT incorporates LLM as an autonomous agent to intelligently insert and refine Trojan components within analog designs based on iterative feedback from a detection model. This feedback loop ensures that the inserted Trojans remain stealthy while successfully evading detection. Experimental results demonstrate that our generated Trojan designs exhibit an average Trojan-activation range of 15.74%, ensuring they remain inactive under most operating voltages, while causing a significant performance degradation of 11.3% upon activation.

Related papers

• TrojanTO: Action-Level Backdoor Attacks against Trajectory Optimization Models [67.06525001375722]
  TrojanTO is the first action-level backdoor attack against TO models.<n>It implants backdoor attacks across diverse tasks and attack objectives with a low attack budget.<n>TrojanTO exhibits broad applicability to DT, GDT, and DC.
  arXiv  Detail & Related papers  (2025-06-15T11:27:49Z)
• An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems [7.333490062088133]
  One of the stealthiest threats is the insertion of a hardware trojan into an IC. Trojans can severely compromise system safety and security. This paper presents a non-invasive method for hardware trojan detection based on side-channel power analysis.
  arXiv  Detail & Related papers  (2024-11-19T18:39:20Z)
• SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection [3.048384587446267]
  Many IC companies outsource manufacturing to third-party foundries, creating security risks such as stealthy analog Trojans. Traditional detection methods, including embedding circuit watermarks or conducting hardware-based monitoring, often impose significant area and power overheads. We propose SPICED, a framework that operates within the software domain, eliminating the need for hardware modifications for Trojan detection and localization.
  arXiv  Detail & Related papers  (2024-08-25T17:07:08Z)
• TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans [21.7375312616769]
  TroLLoc is a novel scheme for IC security closure that employs, for the first time, logic locking and layout hardening in unison. We show that TroLLoc successfully renders layouts resilient, with reasonable overheads, against (i.e., general prospects for Trojan insertion as in the ISPD'22 contest, (ii) actual Trojan insertion as in the ISPD'23 contest, and (iii) potential second-order attacks.
  arXiv  Detail & Related papers  (2024-05-09T07:25:38Z)
• TrojanNet: Detecting Trojans in Quantum Circuits using Machine Learning [5.444459446244819]
  TrojanNet is a novel approach to enhance the security of quantum circuits by detecting and classifying Trojan-inserted circuits. We generate 12 diverse datasets by introducing variations in Trojan gate types, the number of gates, insertion locations, and compilers. Experimental results showcase an average accuracy of 98.80% and an average F1-score of 98.53% in effectively detecting and classifying Trojan-inserted QAOA circuits.
  arXiv  Detail & Related papers  (2023-06-29T05:56:05Z)
• Attention Hijacking in Trojan Transformers [68.04317938014067]
  Trojan attacks pose a severe threat to AI systems. Recent works on Transformer models received explosive popularity. Can we reveal the Trojans through attention mechanisms in BERTs and ViTs?
  arXiv  Detail & Related papers  (2022-08-09T04:05:04Z)
• Game of Trojans: A Submodular Byzantine Approach [9.512062990461212]
  We provide an analytical characterization of adversarial capability and strategic interactions between the adversary and detection mechanism. We propose a Submodular Trojan algorithm to determine the minimal fraction of samples to inject a Trojan trigger. We show that the adversary wins the game with probability one, thus bypassing detection.
  arXiv  Detail & Related papers  (2022-07-13T03:12:26Z)
• Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free [126.15842954405929]
  Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a trigger. We propose a novel Trojan network detection regime: first locating a "winning Trojan lottery ticket" which preserves nearly full Trojan information yet only chance-level performance on clean inputs; then recovering the trigger embedded in this already isolated subnetwork.
  arXiv  Detail & Related papers  (2022-05-24T06:33:31Z)
• Towards Effective and Robust Neural Trojan Defenses via Input Filtering [67.01177442955522]
  Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a simple trigger and targeting only one class to using many sophisticated triggers and targeting multiple classes. Most defense methods still make out-of-date assumptions about Trojan triggers and target classes, thus, can be easily circumvented by modern Trojan attacks.
  arXiv  Detail & Related papers  (2022-02-24T15:41:37Z)
• Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases [87.69818690239627]
  We study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime. We propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples.
  arXiv  Detail & Related papers  (2020-07-31T02:00:38Z)
• Odyssey: Creation, Analysis and Detection of Trojan Models [91.13959405645959]
  Trojan attacks interfere with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Existing Trojan detectors make strong assumptions about the types of triggers and attacks. We propose a detector that is based on the analysis of the intrinsic properties; that are affected due to the Trojaning process.
  arXiv  Detail & Related papers  (2020-07-16T06:55:00Z)
• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks [59.42357806777537]
  trojan attack aims to attack deployed deep neural networks (DNNs) relying on hidden trigger patterns inserted by hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts compared to conventional trojan attack methods.
  arXiv  Detail & Related papers  (2020-06-15T04:58:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.