
During his tenure, Danilo Kostic engineered high-assurance cryptographic primitives and performance optimizations across the awslabs/s2n-bignum and aws/aws-lc repositories. He developed AVX2-optimized assembly routines for ML-KEM, integrating formal verification and correctness proofs to ensure reliability and security. Leveraging C, C++, and assembly language, Danilo improved build system integration, enhanced error handling, and enforced memory safety in cryptographic workflows. His work included dynamic backend selection based on CPU capabilities, constant-time hardening against side-channel attacks, and robust unit testing. These contributions strengthened cross-repo consistency, improved runtime stability, and delivered maintainable, portable cryptographic solutions for production environments.
March 2026 monthly summary: Delivered critical fixes and robustness improvements across two repositories: awslabs/s2n-bignum and aws/aws-lc. Key features delivered include corrections to x86 instruction decoding in the s2n-bignum model and substantial hardening of cryptographic paths in ML-KEM within OpenSSL. Major bugs fixed include spurious event trace and ES segment prefix decoding in the x86 model; NULL pointer validation in ML-KEM EVP encapsulate/decapsulate paths with proper error returns. Overall impact: improved runtime stability, correctness of CPU-emulation, and resilience of cryptographic operations under error conditions. Technologies/skills demonstrated: x86 decoding, ES prefix handling, defensive programming with NULL checks, memory safety, and cryptographic protocol robustness.
March 2026 monthly summary: Delivered critical fixes and robustness improvements across two repositories: awslabs/s2n-bignum and aws/aws-lc. Key features delivered include corrections to x86 instruction decoding in the s2n-bignum model and substantial hardening of cryptographic paths in ML-KEM within OpenSSL. Major bugs fixed include spurious event trace and ES segment prefix decoding in the x86 model; NULL pointer validation in ML-KEM EVP encapsulate/decapsulate paths with proper error returns. Overall impact: improved runtime stability, correctness of CPU-emulation, and resilience of cryptographic operations under error conditions. Technologies/skills demonstrated: x86 decoding, ES prefix handling, defensive programming with NULL checks, memory safety, and cryptographic protocol robustness.
February 2026: Strengthened cryptographic key management in the aws/aws-lc project by delivering a robustness-focused update to PQDSA_KEY set_raw, improving error handling, memory management, and atomicity to prevent partial states during failures. The change enhances security and reliability in key handling across the library.
February 2026: Strengthened cryptographic key management in the aws/aws-lc project by delivering a robustness-focused update to PQDSA_KEY set_raw, improving error handling, memory management, and atomicity to prevent partial states during failures. The change enhances security and reliability in key handling across the library.
January 2026 monthly summary highlighting key features delivered, major bugs fixed, and overall impact across aws/aws-lc and awslabs/s2n-bignum. Focus on business value, reliability, and security improvements achieved through targeted code changes and enhanced testing.
January 2026 monthly summary highlighting key features delivered, major bugs fixed, and overall impact across aws/aws-lc and awslabs/s2n-bignum. Focus on business value, reliability, and security improvements achieved through targeted code changes and enhanced testing.
December 2025 monthly summary focusing on key accomplishments, business value, and technical achievements across the awslabs/s2n-bignum and aws/aws-lc repositories. This period delivered high-assurance cryptographic primitives, improved test reliability, and reinforced cross-repo collaboration and portability of critical components. Key outcomes include performance-oriented ML-KEM AVX2 primitives with formal correctness proofs, and stabilized CI by fixing integration test scripts for two major dependencies.
December 2025 monthly summary focusing on key accomplishments, business value, and technical achievements across the awslabs/s2n-bignum and aws/aws-lc repositories. This period delivered high-assurance cryptographic primitives, improved test reliability, and reinforced cross-repo collaboration and portability of critical components. Key outcomes include performance-oriented ML-KEM AVX2 primitives with formal correctness proofs, and stabilized CI by fixing integration test scripts for two major dependencies.
Month 2025-11: This month focused on performance and correctness for ML-KEM on x86 AVX2. Key deliveries include basemul_k2 and basemul_k3 optimizations with formal proofs of correctness (HolLight) and updated congbound rules. Achieved alignment with the mlkem-native reference to ensure portability and maintainability. Business impact: faster, provably correct ML-KEM paths on AVX2 enable safer, more scalable deployment in performance-critical environments. Technologies demonstrated: x86 AVX2 optimization, formal verification workflows (HolLight), congbound rule development, and cross-repo collaboration.
Month 2025-11: This month focused on performance and correctness for ML-KEM on x86 AVX2. Key deliveries include basemul_k2 and basemul_k3 optimizations with formal proofs of correctness (HolLight) and updated congbound rules. Achieved alignment with the mlkem-native reference to ensure portability and maintainability. Business impact: faster, provably correct ML-KEM paths on AVX2 enable safer, more scalable deployment in performance-critical environments. Technologies demonstrated: x86 AVX2 optimization, formal verification workflows (HolLight), congbound rule development, and cross-repo collaboration.
October 2025 monthly summary for awslabs/s2n-bignum focused on delivering high-value performance and correctness improvements to cryptographic reduction workflows. The key deliverable was an optimized ML-KEM poly_reduce path for x86 AVX2, including assembly-level reduction and formal correctness proofs. The work mirrors the existing mlkem-native implementation to ensure cross-repo consistency and reuse proven approaches. This milestone enhances throughput on AVX2-enabled systems and strengthens cryptographic correctness guarantees, contributing to more reliable TLS crypto workloads.
October 2025 monthly summary for awslabs/s2n-bignum focused on delivering high-value performance and correctness improvements to cryptographic reduction workflows. The key deliverable was an optimized ML-KEM poly_reduce path for x86 AVX2, including assembly-level reduction and formal correctness proofs. The work mirrors the existing mlkem-native implementation to ensure cross-repo consistency and reuse proven approaches. This milestone enhances throughput on AVX2-enabled systems and strengthens cryptographic correctness guarantees, contributing to more reliable TLS crypto workloads.
Month 2025-09 performance summary: Upgraded the aws/aws-lc cryptographic stack with a forward-looking S2N bignum upgrade and corrected a critical ML-DSA sampling bug. The upgrade introduces ARM-specific optimizations, Curve25519 assembly updates, and updated import script with latest CFI directives, improving performance and security posture on ARM platforms. The ML-DSA poly_uniform fix ensures only one SHAKE128 block is squeezed per iteration, preserving sampling integrity and rejection sampling behavior. These changes reduce risk in production cryptographic operations and align with current security standards.
Month 2025-09 performance summary: Upgraded the aws/aws-lc cryptographic stack with a forward-looking S2N bignum upgrade and corrected a critical ML-DSA sampling bug. The upgrade introduces ARM-specific optimizations, Curve25519 assembly updates, and updated import script with latest CFI directives, improving performance and security posture on ARM platforms. The ML-DSA poly_uniform fix ensures only one SHAKE128 block is squeezed per iteration, preserving sampling integrity and rejection sampling behavior. These changes reduce risk in production cryptographic operations and align with current security standards.
Month: 2025-08. This monthly summary highlights key feature deliveries and reliability improvements across two repositories, focusing on security hardening and performance optimization. Delivered constant-time hardening for ML-DSA polynomial arithmetic in aws/aws-lc, and introduced runtime dispatch for native backends based on CPU capability in pq-code-package/mlkem-c-aarch64. These changes strengthen security posture against timing side-channel attacks and optimize execution by leveraging hardware features while maintaining safe fallbacks.
Month: 2025-08. This monthly summary highlights key feature deliveries and reliability improvements across two repositories, focusing on security hardening and performance optimization. Delivered constant-time hardening for ML-DSA polynomial arithmetic in aws/aws-lc, and introduced runtime dispatch for native backends based on CPU capability in pq-code-package/mlkem-c-aarch64. These changes strengthen security posture against timing side-channel attacks and optimize execution by leveraging hardware features while maintaining safe fallbacks.
Monthly work summary for 2025-07 focusing on performance optimization in pq-code-package/mlkem-c-aarch64, delivering AVX2-based assembly implementations for polyvec_basemul and updating related documentation.
Monthly work summary for 2025-07 focusing on performance optimization in pq-code-package/mlkem-c-aarch64, delivering AVX2-based assembly implementations for polyvec_basemul and updating related documentation.
Concise monthly summary for May 2025 focusing on delivering business value and technical excellence across two repositories. The month highlights a mix of performance improvements, expanded vector instruction support, and targeted fixes that improved reliability and throughput, with notable impact on cryptographic and polynomial computation workloads.
Concise monthly summary for May 2025 focusing on delivering business value and technical excellence across two repositories. The month highlights a mix of performance improvements, expanded vector instruction support, and targeted fixes that improved reliability and throughput, with notable impact on cryptographic and polynomial computation workloads.
December 2024 summary for awslabs/s2n-bignum: Focused on label management in assembly to improve uniqueness and readability. Implemented an initial approach to prefix local labels with their file names, then evaluated and reverted to filename-agnostic labels to maintain simplicity and compatibility. This work targeted maintainability, reduced risk of label collisions, and alignment with build tooling expectations.
December 2024 summary for awslabs/s2n-bignum: Focused on label management in assembly to improve uniqueness and readability. Implemented an initial approach to prefix local labels with their file names, then evaluated and reverted to filename-agnostic labels to maintain simplicity and compatibility. This work targeted maintainability, reduced risk of label collisions, and alignment with build tooling expectations.

Overview of all repositories you've contributed to across your timeline