Adapting Constrained Devices for Post-Quantum Cryptography

Introduction The transition to post-quantum cryptography (PQC) poses significant challenges for resource-constrained Internet of Things (IoT) devices, which are often equipped with Trusted Execution Environments (TEEs), secure elements, or other forms of hardware security modules (HSMs). These devices typically operate under strict limitations on processing power, RAM, and flash memory, and in some cases are battery-powered. Adopting PQC algorithms in such environments is difficult due to their substantially larger key sizes and, in some cases, higher computational demands. Consequently, the migration to PQC requires careful planning to ensure secure and efficient key management within constrained platforms. This document provides guidance and best practices for integrating PQC algorithms into constrained devices. It reviews strategies for key storage, ephemeral key management, and performance optimization tailored to low-resource environments. One mitigation technique for storage limitations is seed-based key generation, where only a compact seed is stored instead of the full private key. While this approach conserves memory, it introduces additional computational overhead because full private keys must be derived on demand—illustrating the classic computation-versus-storage trade-off. The document also examines ephemeral key generation in protocols such as TLS, along with techniques to optimize PQC signature operations to improve performance within constrained cryptographic modules. The focus is on PQC in constrained devices, with particular attention to the three algorithms standardized by NIST:

Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) ,
Module-Lattice-Based Digital Signature Algorithm (ML-DSA) , and
Stateless Hash-Based Digital Signature Algorithm (SLH-DSA) .

The Hierarchical Signature System/Leighton–Micali Signature (HSS/LMS) is also considered in the context of firmware signing. Future revisions may extend the scope to additional PQC algorithms, such as the Hamming Quasi-Cyclic (HQC) KEM and the Fast Fourier Transform over NTRU-Lattice-Based Digital Signature Algorithm (FN-DSA) .

Conventions and Definitions The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

Key Management in Constrained Devices for PQC The embedded cryptographic components used in constrained devices are designed to securely manage cryptographic keys, often under strict limitations in RAM, flash memory, and computational resources. These limitations are further exhausted by the increased key sizes and computational demands of PQC algorithms. One mitigation of storage limitations is to store only the seed rather than the full expanded private key, as the seed is far smaller and can derive the expanded private key as necessary. To reduce storage requirements on constrained devices, private keys for Initial Device Identifiers (IDevIDs), Locally Significant Device Identifiers (LDevIDs), and the optional attestation private key can be stored as seeds instead of expanded key material. This optimization does not apply to device certificates or trust anchors, which must be stored in persistent device storage since they are signed public data structures (see ). The terms IDevIDs and LDevIDs are explained in IEEE Std 802.1AR .

Seed Management The seed generated during the PQC key generation function is highly sensitive, as it will be used to compute the private key or decapsulation key. Consequently, seeds must be treated with the same level of security as private keys. To comply with , , and guidelines:

Seed Storage Some PQ key exchange mechanisms use a seed to generate their private keys (e.g., ML-KEM, McEliece, and HQC), and those seeds are smaller than private keys, saving storage space. Some implementations may choose to retain the (small) seed rather than the (larger) private key. As the private key is necessary for cryptographic operations, it can be derived from the seed when needed or retained in a cache within the security module. Seeds must be securely stored within a cryptographic module of the device whether hardware or software-based to protect against unauthorized access. Since the seed can derive the private key, it must be safeguarded with the same level of protection as a private key. For example, according to Section 3.6.3, the seed ξ generated during ML-DSA.KeyGen can be stored for later use with ML-DSA.KeyGen_internal. The choice between storing a seed or an expanded private key involves trade-offs between storage efficiency and performance. Some constrained cryptographic modules may store only the seed and derive the expanded private key on demand, whereas others may prefer storing the full expanded key to reduce computational overhead during key usage. While vulnerabilities like the "Unbindable Kemmy Schmidt" attack demonstrate the risks of manipulating expanded private keys in environments lacking hardware-backed protections, these attacks generally assume an adversary has some level of control over the expanded key format. However, in a hardware-backed protcted environment, where private keys are typically protected from such manipulation, the primary motivation for storing the seed rather than the expanded key is not directly tied to mitigating "Kemmy" attacks. The ML-DSA and ML-KEM private key formats, as specified in and , represent the private key using a seed from which the expanded private key is derived. While these formats rely on the seed for key generation, an constrained cryptographic module may choose to store the expanded private key to avoid the additional computation required for running KeyGen. This choice between storing the seed or the expanded private key has direct implications on performance, as key derivation incurs additional computation. The impact of this overhead varies depending on the algorithm. For instance, ML-DSA key generation, which primarily involves polynomial operations using the Number Theoretic Transform (NTT) and hashing, is computationally efficient compared to other post-quantum schemes. In contrast, SLH-DSA key generation requires constructing a Merkle tree and multiple calls to Winternitz One-Time Signature (WOTS+) key generation, making it significantly slower due to the recursive hash computations involved. Designers of constrained systems must carefully balance storage efficiency and computational overhead based on system requirements and operational constraints. While constrained systems employ various key storage strategies, the decision to store full private keys or only seeds depends on design goals, performance considerations, and standards compliance (e.g., PKCS#11). A challenge arises when importing an existing private key into a system designed to store only seeds. When a user attempts to import an already expanded private key, there is a mismatch between the key format used internally (seed-based) and the expanded private key. This issue arises because the internal format is designed for efficient key storage by deriving the private key from the seed, while the expanded private key is already fully computed. As NIST has not defined a single private key format for PQC algorithms, this creates a potential gap in interoperability. If the seed is not securely stored at the time of key generation, it is permanently lost because the process of deriving an expanded key from the seed relies on a one-way cryptographic function. This one-way function derives the private key from the seed, but the reverse operation, deriving the original seed from the expanded key is computationally infeasible.

Efficient Key Derivation When storing only the seed in a constrained cryptographic module, it is crucial that the device is capable of deriving the private key efficiently whenever required. However, it is important to note that constantly re-deriving the private key for every cryptographic operation may introduce significant performance overhead. In scenarios where performance is a critical consideration, it may be more efficient to store the expanded private key directly instead of only the seed. Higher quality implementations may also retain (cache) recently-used or frequently-used private keys to avoid the computational overhead and delay of deriving the private key from the seed with each request. The key derivation process, such as ML-KEM.KeyGen_internal for ML-KEM or similar functions for other PQC algorithms, must be implemented in a way that can securely operate within the resource constraints of the device. If using the seed-only model, the derived private key should only be temporarily held in memory during the cryptographic operation and discarded immediately after use. However, storing the expanded private key may be a more practical solution in time-sensitive applications or for devices that frequently perform cryptographic operations.

Exporting Seeds and Private Keys Given the potential for hardware failures or the end-of-life of devices containing keys, it is essential to plan for backup and recovery of the cryptographic seeds and private keys. Constrained devices should support secure seed backup mechanisms, ideally leveraging encrypted storage and ensuring that the backup data is protected from unauthorized access. In a disaster recovery scenario, the seeds and private keys should be recoverable private key, provided the proper security measures are in place to prevent unauthorized extraction. There are two distinct approaches to exporting private keys or seeds from a constrained device:

Direct Transfer over TLS In scenarios where the constrained device has sufficient capability to initiate or terminate a mutually authenticated TLS session, the device can securely transfer encrypted private key material directly to another cryptographic module.

Export of Encrypted Seeds and Private Keys In more common constrained device scenarios, for secure exporting of seeds and private keys, a strong symmetric encryption algorithm, such as AES in key-wrap mode (), should be used to encrypt the seed before export. This ensures that the seed remains protected even if the export process is vulnerable to quantum attacks. Operationally, the exported data and the the symmetric key used for encryption must both be protected against unauthorized access or modification.

Security Requirements for Export Operations The encryption and decryption of seeds and private keys must occur entirely within the cryptographic modules to reduce the risk of exposure and ensure compliance to established security standards.

Ephemeral Key Management In protocols like TLS and IPsec, ephemeral keys are used for key exchange. Given the increased size of PQC key material, ephemeral key management will have to be optimized for both security and performance. For PQC KEMs, ephemeral key-pairs are generated from an ephemeral seed, that is used immediately during key generation and then discarded. Furthermore, once the shared secret is derived, the ephemeral private key will have to be deleted. Since the private key resides in the constrained cryptographic module, removing it optimizes memory usage, reducing the footprint of PQC key material in the cryptographic module. This ensures that that no unnecessary secrets persist beyond their intended use. Additionally, ephemeral keys, whether from traditional ECDH or PQC KEM algorithms, are intended to be unique for each key exchange instance and kept separate across connections (e.g., TLS). Deleting ephemeral keying material after use not only optimizes memory usage but also ensures that key material cannot be reused across connections, which would otherwise introduce security and privacy issues. These risks are discussed in more detail in the Security Considerations of . Constrained devices implementing PQC ephemeral key management will have to:

Generate ephemeral key-pairs on-demand from an ephemeral seed stored temporarily within the cryptographic module.
Enforce immediate seed erasure after the key-pair is generated and the cryptographic operation is completed.
Delete the private key after the shared secret is derived.
Prevent key reuse across different algorithm suites or sessions.

Optimizing Performance in Constrained Devices for PQC Signature Algorithms When implementing PQC signature algorithms in constrained cryptographic modules, performance optimization becomes a critical consideration. Transmitting the entire message to the cryptographic module for signing can lead to significant overhead, especially for large payloads. To address this, implementers can leverage techniques that reduce the data transmitted to the cryptographic module, thereby improving efficiency and scalability. One effective approach involves sending only a message digest to the cryptographic module for signing. By signing the digest of the content rather than the entire content, the communication between the application and the cryptographic module is minimized, enabling better performance. This method is applicable for any PQC signature algorithm, whether it is ML-DSA, SLH-DSA, or any future signature scheme. For such algorithms, a mechanism is often provided to pre-hash or process the message in a way that avoids sending the entire raw message for signing. In particular, algorithms like SLH-DSA present challenges due to their construction, which requires multiple passes over the message digest during the signing process. The signer does not retain the entire message or its full digest in memory at once. Instead, different parts of the message digest are processed sequentially during the signing procedure. This differs from traditional algorithms like RSA or ECDSA, which allow for more efficient processing of the message, without requiring multiple passes or intermediate processing of the digest. A key consideration when deploying ML-DSA in cryptographic module is the amount of memory available. ML-DSA, unlike traditional signature schemes such as RSA or ECDSA, requires significant memory during signing due to multiple Number Theoretic Transform (NTT) operations, matrix expansions, and rejection sampling loops. These steps involve storing large polynomial vectors and intermediate values, making ML-DSA more memory-intensive. If an cryptographic module has sufficient memory, this may not be an issue. However, in constrained environments with limited memory, implementing ML-DSA can be challenging. The signer must store and process multiple transformed values, leading to increased computational overhead if the cryptographic module lacks the necessary memory to manage these operations efficiently. To address the memory consumption challenge, algorithms like ML-DSA offer a form of pre-hash using the μ (message representative) value described in Section 6.2 of . The μ value provides an abstraction for pre-hashing by allowing the hash or message representative to be computed outside the cryptographic module. This feature offers additional flexibility by enabling the use of different cryptographic modules for the pre-hashing step, reducing memory consumption within the cryptographic module. The pre-computed μ value is then supplied to the cryptographic module, eliminating the need to transmit the entire message for signing. discusses leveraging Externalμ-ML-DSA, where the pre-hashing step (Externalμ-ML-DSA.Prehash) is performed in a software cryptographic module, and only the pre-hashed message (μ) is sent to the hardware cryptographic module for signing (Externalμ-ML-DSA.Sign). By implementing Externalμ-ML-DSA.Prehash in software and Externalμ-ML-DSA.Sign in an hardware cryptographic module, the cryptographic workload is efficiently distributed, making it practical for high-volume signing operations even in memory-constrained cryptographic modules. The main advantage of this method is that, unlike HashML-DSA, the Externalμ-ML-DSA approach is interoperable with the standard version of ML-DSA that does not use pre-hashing. This means a message can be signed using ML-DSA.Sign, and the verifier can independently compute μ and use Externalμ-ML-DSA.Verify for verification -- or vice versa. In both cases, the verifier does not need to know whether the signer used internal or external pre-hashing, as the resulting signature and verification process remain the same.

Additional Considerations for PQC Use in Constrained Devices Key Rotation and Renewal: In constrained devices, managing the lifecycle of cryptographic keys including periodic key rotation and renewal is critical for maintaining long-term security and supporting cryptographic agility. While constrained devices may rely on integrated secure elements or lightweight HSMs for secure key storage and operations, the responsibility for orchestrating key rotation typically resides in the application layer or external device management infrastructure. Although the underlying cryptographic module may offer primitives to securely generate new key pairs, store fresh seeds, or delete obsolete keys, these capabilities must be integrated into the device’s broader key management framework. This process is especially important in the context of PQC, where evolving research may lead to changes in recommended algorithms, parameters, and key management practices. The security of PQC schemes continues to evolve, with potential risks arising from advances in post-quantum algorithms, cryptanalytic or implementation vulnerabilities. As a result, constrained devices should be designed to support flexible and updatable key management policies. This includes the ability to:

Rotate keys periodically to provide forward-secrecy,
Update algorithm choices or key sizes based on emerging security guidance,
Reconfigure cryptographic profile of the device via firmware updates.

Key Sizes of Post-Quantum Algorithms The key sizes of post-quantum algorithms are generally larger than those of traditional cryptographic algorithms. This increase in key size is a significant consideration for constrained devices, which often have limited memory and storage capacity. For example, the key sizes for ML-DSA and ML-KEM are larger than those of RSA or ECDSA, which can lead to increased memory usage and slower performance in constrained environments. The following table provides the key sizes of some instantiations of ML-DSA, ML-KEM, FN-DSA and SLH-DSA. For comparision we also include the key sizes for X25519 and ED25519, which are traditional schemes widely used in constrained environments.

Algorithm	Type	Size (bytes)
ML-DSA-65	Public Key	1952
	Private Key	4032
	Signature	3309
SLH-DSA-SHA2-192s	Public Key	48
	Private Key	96
	Signature	16224
FN-DSA-512	Public Key	897
	Private Key	1281
	Signature	666
ML-KEM-768	Public Key	1568
	Shared Secret	32
X25519	Public Key	32
	Shared Secret	32
Ed25519	Public Key	32
	Signature	64

Full key sizes for ML-DSA, ML-KEM, FN-DSA and SLH-DSA are specified in , , and respectively.

Post-quantum Firmware Upgrades for Constrained Devices Constrained devices deployed in the field require periodic firmware upgrades to patch security vulnerabilities, introduce new cryptographic algorithms, and improve overall functionality. However, the firmware upgrade process itself can become a critical attack vector if not designed to be post-quantum. If an adversary compromises the update mechanism, they could introduce malicious firmware, undermining all other security properties of the cryptographic modules. Therefore, ensuring a post-quantum firmware upgrade process is critical for the security of deployed constrained devices. CRQCs pose an additional risk by breaking traditional digital signatures (e.g., RSA, ECDSA) used to authenticate firmware updates. If firmware verification relies on traditional signature algorithms, attackers could generate forged signatures in the future and distribute malicious updates.

Post-Quantum Firmware Authentication To ensure the integrity and authenticity of firmware updates, constrained devices will have to adopt PQC digital signature schemes for code signing. These algorithms must provide long-term security, operate efficiently in low-resource environments, and be compatible with secure update mechanisms, such as the firmware update architecture for IoT described in . The Software Updates for Internet of Things (SUIT) working group is defining mandatory-to-implement cryptographic algorithms for IoT devices in , which recommends use of HSS/LMS to secure software devices. Stateful hash-based signature schemes, such as HSS/LMS or the similar XMSS , are good candidates for signing firmware updates. Those schemes offer efficient verification times, making them more practical choices for constrained environments where performance and memory usage are key concerns. Their security is based on the security of the underlying hash function, which is well-understood. A major downside of stateful hash-based signatures is the requirement to keep track of which One-Time Signature (OTS) keys have been reused, since reuse of a single OTS key allows for signature forgeries. However, in the case of firmware updates, the OTS keys will be signing versioned updates, which may make state management easier. discusses various strategies for a correct state and backup management for stateful hash-based signatures. Other post-quantum signature algorithms may also be viable for firmware signing:

SLH-DSA, a stateless hash-based signature specified in , also has well-understood security based on the security of its underlying hash function, and additionally doesn't have the complexities associated with state management that HSS and XMSS have. However, signature generation and verification are comparatively slow, and signature sizes are generally larger than other post-quantum algorithms. SLH-DSA's suitability as a firmware signing algorithm will depend on the capabilities of the underlying hardware.
ML-DSA is a lattice-based signature algorithm specified in . It is more performant than SLH-DSA, with significantly faster signing and verification times, as well as shorter signatures. This will make it possible to implement on a wider range of constrained devices. The mathematical problem underpinning ML-DSA, Module Learning With Errors (M-LWE), is believed to be a hard problem by the cryptographic community, and hence ML-DSA is believed to be secure. Cryptographers are more confident still in the security of hash-based signatures than M-LWE, so developers may wish to factor that in when choosing a firmware signing algorithm.

Hybrid Signature Approaches To enable secure migration from traditional to post-quantum security, hybrid signature methods can be used for firmware authentication. Parallel signatures, where a traditional and a post-quantum signature are generated and attached separately, is simple to implement, requires minimal changes to existing signing, and aligns well with current secure boot and update architectures. Other hybrid techniques, such as cross-linked signatures (where signatures cover each other's values), composite signatures (which combine multiple signatures into a single structured signature), or counter-signatures (where one signature signs over another) introduce more complexity and are not yet typical in resource-constrained firmware workflows.

Impact of PQC Authentication on Constrained Devices In constrained environments, devices are typically assumed to function as clients that initiate outbound connections, authenticating to servers using certificates or raw public keys (). However, some devices also serve in server roles, enforcing local authentication policies. These scenarios require support for both outbound and inbound authentication, and both roles face significant challenges when adopting post-quantum cryptography (PQC). Additionally, verifying digital signatures such as during secure boot or firmware updates is a critical operation for constrained devices, regardless of whether they act as clients or servers. While specific deployment scenarios may differ, the fundamental technical impacts of PQC authentication in constrained devices can be summarized into three main areas:

Larger Signatures and Certificate Sizes Post-quantum signature schemes typically produce much larger public keys and signatures than their traditional counterparts. A comparison is provided in . These larger artifacts introduce several challenges. For example, certificate chains with PQC public keys require more storage, and trust anchors - particularly for schemes like SLH-DSA - may be too large to embed in constrained ROM. Furthermore, validating signed payloads or commands increases network bandwidth requirements. In the case of large hash-based signatures, implementations may adopt streaming verification, where only parts of the message are processed at a time to reduce memory usage. An example of such an approach for SLH-DSA is described in .
Increased RAM usage and performance profile. Post-quantum signature verification often demands significantly more RAM than traditional schemes used for asymmetric cryptography. For example, ML-DSA-65 in its high-performance configuration may require over 68 KB of memory during signing and up to 10 KB during verification on Cortex-M4-class devices. This poses challenges for use cases such as firmware verification (e.g. secure boot) and certificate validation during TLS handshakes or the generation of signed claims about the devices's hardware and software state, a process generally referred to as device attestation. As part of this remote attestation procedure , the device will need to present such claims to a remote peer, signed using an attestation key. To remain secure against CRQCs, the attestation mechanism must also employ quantum-safe cryptographic primitives. Several memory-optimized implementations exist (see ), but they typically trade memory savings for slower performance. For instance, the ML-DSA.Sign operation can be implemented within 8 KB of RAM, though at the cost of significantly increased runtime. Conversely, ML-DSA.Verify can be performed in as little as 3 KB of RAM without a major performance penalty. Devices with 8 - 16 KB of available RAM must often balance performance against feasibility when integrating PQC signature verification.

When constrained devices must authenticate inbound connections, validate commands, or verify stored data, PQC authentication imposes a burden that must be explicitly addressed through selection of schemes with smaller signature sizes (e.g. FN-DSA). These choices should be aligned with the device’s operational profile, available memory, and longevity requirements.

Security Considerations The security considerations for key management in constrained devices for PQC focus on the secure storage and handling of cryptographic seeds, which are used to derive private keys. Seeds must be protected with the same security measures as private keys, and key derivation should be efficient and secure within resource-constrained cryptographic module. Secure export and backup mechanisms for seeds are essential to ensure recovery in case of hardware failure, but these processes must be encrypted and protected from unauthorized access.

Side Channel Protection Side-channel attacks exploit physical leaks during cryptographic operations, such as timing information, power consumption, electromagnetic emissions, or other physical characteristics, to extract sensitive data like private keys or seeds. Given the sensitivity of the seed and private key in PQC key generation, it is critical to consider side-channel protection in cryptographic module design. While side-channel attacks remain an active research topic, their significance in secure hardware design cannot be understated. Cryptographic modules must incorporate strong countermeasures against side-channel vulnerabilities to prevent attackers from gaining insights into secret data during cryptographic operations.

Acknowledgements Thanks to Jean-Pierre Fiset, Richard Kettlewell, Mike Ounsworth, and Aritra Banerjee for the detailed review.