---
title: 'LLMPirate: LLM-Driven Hardware IP Piracy'
url: https://www.emergentmind.com/topics/llmpirate
type: topic
---

# LLMPirate: LLM-Driven Hardware IP Piracy

LLMPirate is an end-to-end, LLM-based framework for hardware IP piracy that targets Verilog gate-level netlists and uses large language models to generate functionally equivalent but structurally modified netlists designed to evade piracy-detection tools. In the GUIDE courseware repository, LLMPirate is also a representative teaching unit under LLM-aided hardware security, described as “LLM-driven rewriting to thwart piracy-detection tools” and positioned as a hands-on lab on maliciously leveraging LLMs for function-preserving structural obfuscation of circuit designs [2411.16111], [2603.17296].

## 1. Definition and conceptual scope

LLMPirate denotes a specific hardware-security attack technique and, in educational use, a standardized instructional unit. The research paper defines it as “the first LLM-based technique able to generate pirated variations of circuit designs that successfully evade detection across multiple state-of-the-art piracy detection tools,” while the GUIDE framework summarizes it as “LLM-driven rewriting to thwart piracy-detection tools” [2411.16111], [2603.17296].

Within GUIDE’s taxonomy, LLMPirate appears under **Topic: LLM-aided Hardware Security**, **Subtopic: Hardware Attacks**, and serves as one of three representative GUIDE units alongside VeriThoughts and enhanced LLM-aided testbench generation. In that curricular setting, LLMPirate teaches that LLMs can be used maliciously, not only for productivity, and frames IP piracy as deliberate, function-preserving structural obfuscation of RTL or gate-level IP rather than incidental code reuse [2603.17296].

The primary object of attack is firm IP in the form of gate-level Verilog netlists. The pirate’s objective is not to synthesize a new design from a specification, but to transform an existing design so that its functionality remains identical while its representation changes enough to defeat similarity-based detection. GUIDE emphasizes this distinction explicitly: the attacker takes a functional design and rewrites or obfuscates it to bypass structural piracy detectors while still implementing the same functionality [2603.17296].

## 2. Threat model and attack formulation

The hardware LLMPirate paper situates the attack in a supply chain in which modern SoCs are assembled from many third-party and in-house IP blocks delivered as RTL or gate-level netlists, and in which untrusted parties such as foundries, integrators, and contractors often see valuable IP. The paper cites Micron’s estimate of \$8.75B losses due to DRAM IP theft in 2018 and U.S. Trade Representative estimates of \$225–600B in annual IP theft, using these figures to motivate hardware IP piracy as an economically significant threat surface [2411.16111].

The threat model is explicitly black-box against the detection tools. The attacker has no access to detector internals such as GNN weights, training data, hash parameters, or algorithm details, but can submit designs to the detector and observe similarity scores or binary piracy labels. The detection tools are fixed; the attacker cannot retrain or poison them. The attack is therefore mounted entirely on the input design: given a victim netlist, the attacker rewrites it while preserving functionality and obeying design rules, with the goal of driving detector similarity below the operational thresholds used by the tools [2411.16111].

The attacker is assumed to possess the original victim netlist in Verilog form, black-box access to one or more LLMs, and local checking infrastructure such as simulation and, optionally, formal equivalence checking. The concrete evasion targets are tool-specific: for GNN4IP, cosine similarity must be pushed below \(0\); for MOSS below \(0.2\); and for Jplag and SIM below \(0.3\). The paper focuses on gate-level netlists rather than high-level RTL because manual rewriting of gate-level logic while preserving behavior is difficult and because GNN4IP operates on gate-level netlists converted to graphs [2411.16111].

## 3. Technical architecture and methodology

LLMPirate is organized around three stated challenges: generic LLMs do not understand gate-level Verilog well, large netlists exceed practical context limits, and one-shot LLM outputs are non-deterministic and error-prone. The framework addresses these with three corresponding solutions: prompt syntax translation, pre-characterization with divide-and-conquer rewriting by gate type, and a feedback-guided interactive loop [2411.16111].

The first solution rewrites gate-level Verilog into generic Boolean formulas that are closer to textbook Boolean algebra. A canonical example in the paper is the translation of Verilog `or U1 (c, a, b);` into the prompt representation `c = OR(a, b)`. Formally, the naive task is written as
$$
R \sim p(R \mid Q, \theta),
$$
where \(Q\) is the raw Verilog prompt, \(R\) the response, and \(\theta\) the model parameters. After syntax translation, the formulation becomes
$$
R \sim p(R \mid \mathcal{T}(Q), \theta),
$$
where \(\mathcal{T}(Q)\) denotes the Boolean-form transformation of the input [2411.16111].

The second solution addresses scalability by pre-characterizing the original netlist and extracting the set \(G\) of unique gate types. Rather than asking an LLM to rewrite an entire circuit, LLMPirate constructs a small representative Boolean problem for each gate type and each allowed operator set. The paper gives the allowed operator sets as follows: **AND** can be implemented with \([NAND]\), \([NOR]\), or \([OR, NOT]\); **OR** with \([NAND]\), \([NOR]\), or \([AND, NOT]\); **NAND** with \([NOR]\), \([AND, NOT]\), or \([OR, NOT]\); **NOR** with \([NAND]\), \([AND, NOT]\), or \([OR, NOT]\); and **XOR** and **XNOR** with \([NAND]\) or \([NOR]\). The local rewriting task is expressed as
$$
R_i \sim p(R_i \mid \mathcal{T}(Q_i), \theta), \quad \forall i \in \{1,2,\ldots,|G|\}.
$$
This divide-and-conquer decomposition removes the full-netlist context bottleneck and localizes the LLM’s role to verified Boolean rewrites of gate types [2411.16111].

The third solution is a feedback-guided interactive formulation. For each gate type and operator set, LLMPirate allows up to \(M=5\) attempts. Each candidate response is checked for syntactic validity, compliance with the allowed operator set, and functional equivalence under exhaustive simulation of all \(2^n\) input assignments for an \(n\)-input gate. Failed outputs are converted into short corrective prompts and re-submitted. The interactive process is written as
$$
p(R_i^j \mid \mathcal{C}(R_i^{j-1}) \oplus \mathcal{T}(Q_i^j), \theta), \quad
j \in \{1,2,\ldots,M\}, \quad
\forall i \in \{1,2,\ldots,|G|\},
$$
where \(\mathcal{C}(R_i^{j-1})\) denotes feedback extracted from the previous attempt and \(\oplus\) denotes concatenation [2411.16111].

Once validated transformations are stored in a dictionary, LLMPirate rewrites an entire netlist gate-by-gate. The paper describes five mapping strategies for selecting replacements: **AND_NOT**, **NAND**, **NOR**, **OR_NOT**, and **random**. The random strategy selects among all valid transformations for a gate type and is intended to increase irregularity in the resulting structural pattern. Whole-netlist equivalence is then established in two ways: verified local replacement guarantees correctness by composition, and for 31 benchmark netlists formal equivalence is additionally checked with Cadence Conformal Equivalence Checker [2411.16111].

## 4. Evaluation, detectors, and empirical findings

The experimental study integrates eight off-the-shelf LLMs: CoPilot, GPT-3.5-Turbo-16k, GPT-4-Turbo, Claude-3-Opus-20240229, Gemini-1.0-Pro, CodeLlama-7B-Instruct-hf, CodeLlama-13B-Instruct-hf, and Llama-3-8B-Instruct. The evaluation uses 31 Verilog netlists from the GNN4IP repository and three large case studies—IBEX, MOR1KX, and a GPS module—with gate counts ranging from a few hundred gates to approximately \(17\)k in the core set and approximately \(158\)k and \(193\)k in the larger case studies [2411.16111].

The detectors span graph-based and text-based paradigms.

| Tool | Paradigm | Output / threshold |
|---|---|---|
| GNN4IP | Graph Neural Network-based hardware piracy detector | similarity in \([-1,1]\); threshold \(0\) |
| MOSS | Code plagiarism via winnowing | similarity in \([0,1]\); threshold \(0.2\) |
| Jplag | Code plagiarism via Greedy-String-Tiling | similarity in \([0,1]\); threshold \(0.3\) |
| SIM | Generic text/software similarity via tokenization + LCS | similarity in \([0,1]\); threshold \(0.3\) |

Jplag and SIM are used in text mode rather than a Verilog-aware mode. For each netlist, each LLM, and each mapping strategy, LLMPirate generates \(N=5\) pirated variants; results are reported both as per-LLM distributions and as best-achieved scores over models and strategies [2411.16111].

The abstract states that LLMPirate is able to “consistently evade detection on 100% of tested circuits across every detection tool.” In the detailed results, Table I reports total evasion rates of **100%** for **GNN4IP**, **MOSS**, and **Jplag**, and **81.25%** for **SIM**, with the paper attributing the SIM caveat to a high false-positive rate on Verilog because SIM does not support Verilog natively and overreacts to repeated keywords such as `and` and `nand`. Against GNN4IP, GPT-4 and CoPilot are described as the best performers; GPT-3.5 and Claude also perform well; CodeLlama-13B substantially outperforms CodeLlama-7B; and Gemini, Llama3-8B, and CodeLlama-7B are weaker, with CodeLlama-7B and Llama3-8B succeeding on only 10 and 11 of 32 netlists, respectively [2411.16111].

The random mapping strategy yields the most successful evasions and the lowest average similarity scores, which the paper attributes to the irregularity produced by randomly selecting among multiple valid transformations per gate type. The feedback-guided multi-attempt loop also materially affects capability: after all five attempts, GPT-4, CoPilot, Claude, and GPT-3.5 achieve 33, 28, 23, and 21 successful gate-type transformations, respectively, while smaller models show large percentage improvements across attempts, including approximately \(700\%\) improvement for Llama3-8B from attempt 1 to attempt 5 [2411.16111].

The case studies demonstrate scale. On IBEX, 7 out of 8 LLMs produce pirated designs that GNN4IP classifies as “not pirated.” On MOR1KX and the GPS module, LLMPirate generates pirated netlists in seconds after transformations are cached and evades MOSS, Jplag, and SIM with low similarity, but GNN4IP labels these large pirated netlists as pirated; the paper interprets this as evidence of GNN4IP bias on large graphs because the similarity between each original design and itself is already about \(0.97\). The reported overheads are typically around \(50\%\) increase in gate count and around \(20\%\) increase in critical path length, with larger gate-count overheads for XOR/XNOR-heavy circuits such as c499 variants [2411.16111].

## 5. LLMPirate in digital design education

In GUIDE, LLMPirate is not only a research artifact but also a teaching-ready unit with a standardized structure comprising slides, a short video, a runnable Google Colab or script, and related papers. GUIDE requires units to be runnable from scratch, include an end-to-end example, define grading evidence, and follow stable file naming and layout. LLMPirate is integrated into the **GUIDE4HardwareSecurity** semester plan in **Week 6**, where the stated focus is “LLM-driven IP piracy and obfuscation techniques; evading detection tools” [2603.17296].

Pedagogically, the unit begins with naive prompting: students supply original Verilog circuits to an LLM and ask it to rewrite or obfuscate them while preserving I/O behavior. This deliberately exposes limitations, including failures on large-scale Verilog code, context-window constraints, and the tendency to produce either superficial changes or incorrect logic. The lab then introduces tool-driven evaluation through formal equivalence checking and piracy-detection tools, giving students two quantitative signals: a functional equivalence result and a structural similarity score [2603.17296].

The central instructional move is to place Boolean netlist representations inside the prompt and instruct the model to perform gate-level transformations. GUIDE states that students “will utilize a Boolean representation of the circuit netlists within the prompt, instructing the model to perform gate-level transformations,” and then enhance the framework by defining iterative feedback prompts that incorporate the outputs of the provided tools. The workflow is therefore explicitly tool-in-the-loop: LLM output is checked independently, failures are fed back as prompt context, and the circuits are re-evaluated for functionality and piracy evasion after each iteration [2603.17296].

The student deliverables are also defined precisely. Students submit: **(i)** their final prompting framework consisting of the initial prompt and all tool-feedback prompts, **(ii)** the functional equivalence and structural similarity scores for all tested Verilog circuits, and **(iii)** an evaluation of scores after each framework implementation, assessing which configuration best facilitated IP piracy. In GUIDE’s broader curriculum, LLMPirate complements VeriThoughts, enhanced testbench generation, and hardware-Trojan units such as GHOST, ATTRITION, TrojanLoC, NOODLE, and SALAD, thereby contributing to an attack–defense–governance narrative in LLM-aided hardware security [2603.17296].

## 6. Limitations, defenses, and broader uses of the term

The hardware LLMPirate paper identifies several constraints. Detector-specific thresholds shape the quantitative notion of success; SIM suffers from false positives on Verilog; and GNN4IP exhibits a strong large-graph bias that complicates interpretation on very large designs. An ablation study further shows that without syntax translation and pre-characterization, LLMPirate fails entirely, while the feedback-guided loop is an important but not sufficient improvement. GUIDE adds a classroom-oriented articulation of the same problem: initial LLM attempts on large-scale Verilog are limited by context windows and training challenges, making Boolean-netlist prompting and iterative tool feedback necessary [2411.16111], [2603.17296].

The defense discussion is correspondingly architectural. The paper suggests re-training or robustifying ML detectors with LLMPirate-style transformed netlists, rethinking similarity metrics in favor of more abstract representations such as BDDs or SAT-based functional equivalence classes, and testing future detectors against LLM-generated adversarial examples. It also notes that watermarking LLM outputs may be ineffective because LLMPirate uses LLM responses as intermediate structures rather than as final emitted text [2411.16111].

Elsewhere in the literature, “LLMPirate” is used more loosely as a descriptor for offensive or piracy-like LLM behavior. The LLM fingerprinting paper states that a “LLMPirate” tool would be essentially LLMmap packaged for offensive reconnaissance against LLM-integrated applications; the copyright-forensics paper describes Copyright Detective as a toolkit for catching “LLMPirate” behavior in memorization and copyright leakage; and the multi-host network-attack paper frames its question as whether an LLM equipped with tools could behave like an automated red-team operator, an “LLMPirate.” This suggests that the term has expanded beyond the specific hardware IP piracy framework into a broader label for adversarial LLM use in reconnaissance, exfiltration, and functionally abusive automation [2407.15847], [2602.05252], [2501.16466].

Across these settings, a common theme is that raw model capability is often insufficient without a surrounding system of representations, tools, and feedback loops. In hardware IP piracy, that system consists of Boolean syntax translation, gate-type decomposition, and equivalence-guided iteration; in autonomous network attack, it consists of high-level actions, an environment state service, and an attack-graph service. A plausible implication is that LLMPirate is best understood not as a single prompt pattern but as a class of tool-in-the-loop offensive workflows in which LLMs are coupled to domain-specific transformations and verification infrastructure [2411.16111], [2501.16466].

Source: https://www.emergentmind.com/topics/llmpirate