FMX Encryption in Robotics

• FMX Encryption is a dual-layer system that combines LCG-based masking with Blowfish-ECB to protect sensitive data in humanoid robotics.
• The predictable LCG and static key in ECB mode expose vulnerabilities, enabling pattern analysis and brute-force attacks.
• Key challenges include potential fleet-wide breaches, remote exploits, and integration issues with compile-time ciphertext synthesis frameworks.

FMX Encryption is a proprietary, dual-layer encryption system designed for the protection of sensitive configuration and telemetry data, notably in commercial humanoid robotics. Its architecture combines a pseudo-random obfuscation stage based on a Linear Congruential Generator (LCG) with a symmetric block cipher (Blowfish) operating in Electronic Code Book (ECB) mode. FMX is also discussed as an integration target for compile-time ciphertext synthesis frameworks that leverage efficient data ingestion and algebraic reuse in encrypted environments. The cryptographic and operational characteristics of FMX have direct implications for security, privacy, and the deployment of cyber-physical agents in critical infrastructure.

1. Architectural Design of FMX Encryption

FMX encryption consists of two distinct sequential stages:

1. LCG-Based Masking (Inner Layer) The plaintext data $P$ is masked using bytes generated from an LCG defined by the recurrence:

$X_{n+1} = (a \cdot X_n + c) \bmod 2^{32}$

Typical parameter choices replicate standard software implementations (e.g., $a = 1664525$, $c = 1013904223$, $m = 2^{32}$). Each byte of plaintext $P_i$ is XOR-ed with a mask byte $\text{mask}[i] = f(X_i)$, which is derived from the high-order bits or other functions of $X_n$. This step serves as an obfuscation mechanism rather than a cryptographically secure transformation.

1. Blowfish-ECB Layer (Outer Layer) The obfuscated data $P'$ is segmented into 64-bit blocks and encrypted with Blowfish in ECB mode, using a static fleet-wide 128-bit key:

$$C_i = \operatorname{Blowfish}_{\text{ECB}}(P'_i, \text{key})$$

The fixed key is hard-coded across all units, and ECB mode causes identical plaintext blocks to yield identical ciphertexts.

FMX’s process can be summarized:

• $P' = P \oplus \text{MASK}$ (with MASK from the LCG)
• $$C = \operatorname{Blowfish}_{\text{ECB}}(P', \text{key})$$

2. Cryptographic Analysis and Known Vulnerabilities

FMX’s layered encryption introduces several critical weaknesses:

Component	Weakness	Exploitability
LCG Mask	Predictable, limited seed	Brute-force / reversal
Blowfish-ECB	Static fleet-wide key	Key extraction
ECB mode	No block randomization	Pattern analysis

• LCG Predictability:

The seed space ($2^{32}$) is feasible for brute-force attacks. Parameter reuse enables attackers, upon discovery of the initial state or derivation mechanism, to reconstruct or invert the mask, nullifying the obfuscation.

• Static Key and ECB Weaknesses:

The use of a single key across devices, once compromised via firmware extraction or device-level reverse engineering, enables adversaries to decrypt all FMX-protected data fleet-wide. ECB leaks data structure, as identical blocks encrypt identically, making statistical analysis and block reassembly straightforward.

• Combined Impact:

Once an attacker obtains either the key or mask parameters, decryption is trivial. The resultant attack surface includes the confidentiality of telemetry, system configuration, and potential command sequences.

3. Data Flow and Application Scenarios in Humanoid Robotics

FMX encryption secures:

• Sensor telemetry:

Continuous multi-modal streams (audio, video, position) are transmitted at regular intervals (e.g., every 300 seconds) to designated network endpoints.

• Service-state and configuration:

Internal states and parameter files that enable remote diagnostics, updates, or actuator commands.

Compromised FMX enables:

• Data exfiltration:

Sensitive data, intended to be protected, becomes accessible through decrypted traffic, violating privacy and regulatory frameworks (notably GDPR Articles 6 and 13).

• Remote exploitation:

Attackers can intercept and alter configuration or operational commands, potentially impacting robotic behavior and the integrity of physical processes.

• Lateral movement and cyber-physical escalation:

Access to one unit may allow adversaries control over orchestrated fleets, providing a basis for physical and digital attacks on connected critical infrastructure.

4. Security Consequences and Risk in Physical-Cyber Systems

The weaknesses in FMX’s cryptographic scheme raise several concerns:

• Fleet-wide compromise risk:

The static key architecture means whole ecosystems of robots become vulnerable from a single compromised device or firmware leak.

• Attack vector expansion:

Patterns leaked by ECB mode and mask predictability could facilitate automated decryption and traffic characterization.

• Weaponization potential:

Humanoid robots, due to vulnerabilities in FMX, can be converted from passive agents (surveillance, telemetry) to active offensive assets capable of reconnaissance, lateral movement, and physical intervention.

Implications extend to industrial settings (factories, power plants), where robot fleets orchestrate operational tasks. The paper documents cases where adaptive Cybersecurity AI (CAI) agents pivot from reconnaissance to offensive action against central control planes, illustrating real-world risk (Mayoral-Vilches, 17 Sep 2025).

5. Integration with Compile-Time Ciphertext Synthesis

FMX encryption is identified as a target for compile-time ciphertext synthesis frameworks, such as those described for fully homomorphic encryption (FHE):

• Compile-time basis synthesis:

Encryption is shifted offline by precomputing encrypted basis vectors ($c_i = \text{Enc}(e_i)$) and synthesizing encryptions of arbitrary vectors through runtime linear combinations:

$$\text{SynthEnc}(m) = \sum_{i=1}^d m_i \cdot c_i + r$$

Here, $r$ is a random encryption of zero, incorporating necessary randomness for IND-CPA security.

• Operational efficiency:

This method leverages homomorphic addition and scalar multiplication, reducing online encryption costs and enabling high-throughput data ingestion (e.g., batch records in encrypted databases, streaming pipelines).

• Compatibility with FMX:

While FMX itself does not natively support FHE primitives, the notion of compile-time encrypted data blocks and symbolic evaluation is compatible with the integration of FMX into secure compiler backends and encrypted database ingestion mechanisms (Zhao, 19 May 2025).

A plausible implication is that if FMX were to evolve toward more sophisticated cryptographic foundations (e.g., dynamic keys, authenticated encryption), compile-time synthesis techniques would enhance efficiency and algebraic flexibility.

6. Countermeasures and Evolution of Defensive Architectures

Given FMX’s vulnerability profile, adaptive CAI-powered defenses are highlighted as necessary:

• Continuous behavior and traffic monitoring:

CAI agents can detect novel exfiltration or anomalous robot behaviors, responding in real time.

• Automated red-teaming and malware emulation:

Offensive simulation against deployed robots aids in vulnerability assessment and rapid response.

• Dynamic threat adaptation:

Learning algorithms absorb new intrusion patterns, supporting automated remediation even as cryptosystems are compromised.

The paper advocates for replacing FMX-like schemes with robust alternatives:

• Use dynamic, device-specific symmetric keys with per-session entropy.
• Implement block ciphers in secure modes (CBC, GCM) to prevent pattern leakage.
• Employ strong, non-predictable masking constructs or eliminate the masking stage in favor of proper cryptographic randomization.

7. Summary and Future Directions

The FMX encryption scheme, with its LCG-based masking and static Blowfish-ECB configuration, fails to provide strong cryptographic guarantees. Its vulnerabilities—predictable masking, static keys, and block cipher mode—make it unsuitable for environments that require confidentiality, integrity, and robust access control, such as humanoid robotics in critical infrastructure. Empirical research indicates the risk of these systems being converted into attack vectors and surveillance platforms.

Future directions include:

• Adoption of modern cryptographic standards for robotic platforms.
• Integration with compile-time ciphertext synthesis for efficient and secure data ingestion.
• Deployment of adaptive CAI frameworks to preempt and respond to evolving cyber-physical threats.
• Continuous empirical risk assessment to inform future standardization and regulatory oversight for physical-cyber convergence systems.