Pre-Silicon Security Verification

• Pre-silicon security verification is the practice of validating digital hardware designs before fabrication by identifying security properties and mitigating vulnerabilities.
• It integrates formal methods, simulation-based tests, and machine learning to uncover logic bugs, hardware Trojans, and side-channel leaks.
• Effective workflows combine asset identification, threat modeling, and automated property generation to enhance risk management in semiconductor design.

Pre-silicon security verification is the discipline and practice of identifying, modeling, and exhaustively validating security properties, threat mitigations, and security-critical behaviors in digital hardware designs prior to tape-out. It is essential for eliminating both accidental vulnerabilities (logic bugs) and deliberate subversions (hardware Trojans, supply-chain attacks, side-channels, privilege escalations) before hardware becomes immutable through manufacturing. The field comprises a diverse set of formal, simulation-based, and coverage-driven methodologies, increasingly augmented by machine learning and LLMs, and now forms a cornerstone of modern semiconductor design assurance.

1. Foundations and Goals of Pre-Silicon Security Verification

The primary objective of pre-silicon security verification is to ensure that a hardware design, described at the register-transfer level (RTL) or netlist level, robustly enforces defined security properties and is free from classes of implementation flaws exploitable by adversaries post-fabrication (Ganesan et al., 2021, Ahmed et al., 2022, Geier et al., 11 Nov 2025). Key targets include logic bugs that undermine confidentiality, integrity, or availability; hardware Trojans that enable unauthorized access or data exfiltration; side-channel leakages (timing, microarchitectural, power, fault) that circumvent functional correctness; and vulnerabilities introduced by platform integration or IP composition.

Verification is performed before tape-out to avoid the prohibitive costs and risks of post-silicon bug discovery. Current pre-silicon security flows encompass:

• Asset identification: rigorous enumeration of all security-relevant data paths, registers, logic, and inter-block dependencies (Hasan et al., 6 Jan 2026, Nath et al., 7 Feb 2025).
• Threat modeling: mapping adversarial models (physical, logical, supply-chain) to assets.
• Property generation: systematic translation of security requirements into temporal logic (LTL/CTL), SystemVerilog Assertions (SVA), or coverage metrics (Ankireddy et al., 22 Jun 2025, Rogers et al., 2024).
• Security property verification: via model checking, simulation-based testbenches, or fuzzing frameworks.
• Fault and side-channel resilience analysis: assessing susceptibility to physical or microarchitectural attacks (Geier et al., 11 Nov 2025, Nasahl et al., 2022).
• Integration of automation and machine intelligence to scale coverage and reduce human error (Hasan et al., 6 Jan 2026, Saha et al., 25 Jun 2025).

Verification workflows are underpinned by formal specifications of the security properties, attacker models, and threat mitigations, and increasingly incorporate feedback between property generation, testbenches, and coverage evaluation for completeness.

2. Security Asset Identification and Property Specification

Security asset identification forms the front end of the verification flow and heavily influences subsequent tasks such as threat modeling and property synthesis. Automated frameworks, such as LAsset, leverage structured parsing, LLM-based context learning, and hierarchical dependency graphs to extract both primary (registers, ports, key storage) and secondary (control, configuration, derived) assets from RTL and specification documents (Hasan et al., 6 Jan 2026). The framework establishes a mapping:

$R_s \subseteq \mathcal{C} \times \mathcal{S}$

where $\mathcal{C}$ are conceptual assets (confidential key, status registers, etc.) and $\mathcal{S}$ are RTL signals or storage elements. Asset dependencies are captured as a graph $G=(V,E)$ interlinking intra- and inter-module security flows, and annotated with quantitative "degree of influence" (DoI) metrics.

Parallel research addresses automated primary asset identification in Verilog designs using pattern-based and behavioral heuristics, yielding search-space reductions and quantifiable recall and precision across benchmark IP families (Crypto, GPIO, Peripheral) (Nath et al., 7 Feb 2025). Automatic asset identification enables scaling security analysis to complex SoCs, supporting a shift from expert-driven to agentic flows.

Once assets are identified, property specification uses temporal logic, SVA, PSL, or custom contract formalisms to encode requirements such as confidentiality (non-interference), integrity, access enforcement, liveness, and resilience for verification backends (Ankireddy et al., 22 Jun 2025, Rogers et al., 2024). Robustness is improved by formal vacuity checks that filter trivial or irrelevant properties.

3. Formal Verification, Fuzzing, and Coverage-Driven Approaches

A central pillar in pre-silicon security verification is the use of formal methods—especially model checking—to exhaustively validate temporal properties under all possible inputs and adversarial actions. State-of-the-art flows (e.g., LASA) use LLMs to generate LTL properties or SVA from design documentation and then employ model checkers such as JasperGold to attain high checker and formal coverage (often >88%) (Ankireddy et al., 22 Jun 2025). Iterative feedback between property generation and coverage maximizes assertion reach, detects non-obvious security bugs (e.g., privilege escalations, logic errors), and minimizes vacuous proofs.

Symbolic Quick Error Detection (SQED) and its extensions break traditional bounded model checking limits by introducing symbolic initial states with QED constraints, thus efficiently exposing long-activation bugs and hardware Trojans even in deep pipelines or superscalar cores (Ganesan et al., 2021). The formal SQED problem is:

$$\exists\,s_0,\dots,s_k:\;\bigwedge_{i=0}^{k-1} \bigl(s_{i+1}=T(s_i,i)\bigr)\;\wedge\;\neg P(s_k)$$

where $P$ expresses QED consistency and $T$ is the processor's transition relation, subject to constraints ensuring only reachable, QED-valid states are considered.

For processor side-channel vulnerabilities, coverage-guided pre-silicon fuzzing exploits a self-compositional framework. It executes two hardware instances in lockstep with secret variation and measures microarchitectural state divergence using self-composition deviation (SCD) coverage metrics (Geier et al., 11 Nov 2025). Cumulative SCD coverage is correlated with time-to-leakage detection, enabling prioritization strategies (e.g., weighted feedback) to accelerate bug discovery on complex out-of-order cores.

Simulation-based security verification is further enhanced by multi-agent LLM frameworks (e.g., SV-LLM), which automate the full pipeline—asset recognition, testbench generation, property extraction, vulnerability triage, and testbench-driven bug validation—achieving significant speedups and high detection accuracy (Saha et al., 25 Jun 2025).

4. Fault Analysis, Side-Channel and Trojan Resilience

Accurate pre-silicon evaluation of physical attack resistance is vital for secure hardware. SYNFI demonstrates a SAT-based, netlist-level methodology for fault injection modeling and countermeasure validation post-synthesis, bridging the gap between classical functional verification and physical security (Nasahl et al., 2022). The technique constructs a differential miter and encodes properties such as:

$$\varphi_{\text{unspec}} = (O_{NF} = O_E) \wedge (A_{NF}=0) \wedge (O_F \neq O_E) \wedge (A_F=0)$$

allowing SAT-based resilience checks under single/multiple gate-faults, stuck-at conditions, and logic redundancy. Detailed studies of OpenTitan's AES block highlight the importance of redundancy, encoding choice, and post-synthesis protection to guarantee minimum-fault thresholds for security-critical state transitions.

Complementary approaches analyze microarchitectural side channels using contract-based self-composition and attacker models (e.g., cycle-count distinguishability), with formal hardware–software leakage contracts specifying attacker-observable behaviors. Fuzzing engines are guided by custom coverage functions that reward input programs increasing microarchitectural divergence associated with side-channel leakages (Geier et al., 11 Nov 2025).

5. Platform-Level Verification and Quantifiable Assurance

Moving from individual IPs to SoC-level ("platform") security necessitates accounting for threat interactions, resource sharing, and integration parameters (e.g., bus contention, topology, glue logic, regulator noise) that modulate vulnerability post-IP integration (Ahmed et al., 2022). Platform security is quantified using:

$$S_{\text{platform}} = \sum_{i=1}^N w_i S_{IP_i} - \sum_{k=1}^K \lambda_k \Delta S(P_k)$$

with weights for IP criticality, penalty factors for integration parameter impact, and threat-aware cross-impact matrices reflecting interdependence between mitigations (e.g., a wrapper strengthening SAT-resilience but increasing Trojan attack surface).

Empirical analyses quantify the contribution of integration choices to platform-wide metrics such as SAT attack time or power side-channel divergence, and formalize multi-threat optimization problems as mixed-integer or heuristic programming objectives.

6. Security Property Libraries and Reproducibility

Reproducibility and broad coverage of security properties remain critical for scaling verification confidence. SystemVerilog Assertion libraries, organized by design and CWE, and tagged with vulnerability origins, form a foundation for property-based formal verification, testbench-driven simulation, and property-mining (Rogers et al., 2024). These libraries follow rigorous methodologies for property development—from secure-behavior specification to bug-targeting assertions—enabling consistent integration into both simulation and formal verification environments.

Empirical results demonstrate high bug detection rates in open-source SoCs (e.g., OpenPiton, PULPissimo) and underscore reproducibility challenges in the absence of shared property sets.

7. Automation, Machine Intelligence, and Future Directions

Automation is rapidly transforming pre-silicon security verification. Agentic LLM multi-agent systems integrate technical retrieval, property synthesis, self-consistency checks, threat modeling, coverage measurement, scenario/testbench generation, and vulnerability detection—enabling decoupling of verification quality from manual expertise (Hasan et al., 6 Jan 2026, Saha et al., 25 Jun 2025). Speed-ups on asset identification (up to 30×), property generation, and bug validation are reported, while maintaining high analytical precision and recall.

Emerging best practices include:

• Iterative, feedback-driven prompt and property refinement, integrated with toolchain coverage metrics (Ankireddy et al., 22 Jun 2025).
• Cross-layer composition of formal security guarantees for HW/SW co-designs (e.g., remote attestation, secure boot) using modular LTL and CTL properties, C-level ACSL/Hoare logic, and hardware-software interface abstraction (Dave et al., 2023, Nunes et al., 2018).
• Quantitative metrics and formal frameworks for platform-level security optimization and assurance (Ahmed et al., 2022).
• Integration with post-silicon results and continuous learning loops to incrementally raise verification fidelity and responsiveness to new threat landscapes.

Current limitations concern model scalability for large designs, semantic gaps in property synthesis (timing alignment, dynamic flows), and reliance on high-quality natural-language specifications. Ongoing research targets robust semantic asset extraction, comprehensive dynamic information-flow tracking, and use of feedback from in-field bug discovery for continuous improvement. Notably, bridging the gap between assertion-based verification, coverage-guided dynamic testing, and platform-wide threat modeling remains an active and evolving frontier in pre-silicon security verification.

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References:

• "Effective Pre-Silicon Verification of Processor Cores by Breaking the Bounds of Symbolic Quick Error Detection" (Ganesan et al., 2021)
• "Coverage-Guided Pre-Silicon Fuzzing of Open-Source Processors based on Leakage Contracts" (Geier et al., 11 Nov 2025)
• "LASA: Enhancing SoC Security Verification with LLM-Aided Property Generation" (Ankireddy et al., 22 Jun 2025)
• "LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification" (Hasan et al., 6 Jan 2026)
• "Toward Automated Potential Primary Asset Identification in Verilog Designs" (Nath et al., 7 Feb 2025)
• "FVCARE: Formal Verification of Security Primitives in Resilient Embedded SoCs" (Dave et al., 2023)
• "Security Properties for Open-Source Hardware Designs" (Rogers et al., 2024)
• "SV-LLM: An Agentic Approach for SoC Security Verification using LLMs" (Saha et al., 25 Jun 2025)
• "Quantifiable Assurance: From IPs to Platforms" (Ahmed et al., 2022)
• "SYNFI: Pre-Silicon Fault Analysis of an Open-Source Secure Element" (Nasahl et al., 2022)
• "Formally Verified Hardware/Software Co-Design for Remote Attestation" (Nunes et al., 2018)