ROCA and Infineon TPM chips

This affects IoT devices relying on TPMs for security, especially those that are hard to update using a firmware update.

ROCA: Return Of the Coppersmith Attack.

A ‘Fast Primes’ algo is used to generate primes, but since they are not uniformly distributed, one can use knowledge of the public keys to guess the private keys. Primality testing was discussed in this blog here.

From the full paper here:  Practical Factorization of Widely Used RSA Moduli

  • All RSA primes (as well as the moduli) generated by the RSALib have the following form: p=k∗M+(65537^a modM). Because of this the public key, p*q is of the form 65537^c mod M.
  • The integers k, a are unknown, and RSA primes differ only in their values of a and k for keys of the same size. The integer M is known and equal to some primorial M = Pn# (the product of the n successive primes) and is related to the size of the required key. The attack replaces this with a smaller M’ which still satisfies the above prime generation equation, enabling guessing of a.
  • Coppersmith algorithm based RSA attacks are typically used in scenarios where we know partial information about the private key (or message) and we want to compute the rest. The given problem is solved in the three steps:

    problem → f(x)≡0modp → g(x)=0 → x0

    p is an unknown prime and x0 is a small root of the equation. Code based on SageMath (python) –

  • The LLL is a fascinating algorithm which ‘reduces’ a lattice basis b0, · · · ,bn−1. The algorithm computes an alternative basis b0 ′ , · · · , bn-1′ of the lattice which is smaller than the original basis. Coppersmith’s algorithm utilizes the LLL algorithm.


Discussion of Coppersmith attack in “Twenty Years of Attacks on the RSA Cryptosystem”.

A statement by Infineon is here. A discussion on this appeared on

 Unrelated to this but a wifi attack appeared around the same time.


Ethereum Security and the DAO Solidity Attack

The basics of Ethereum are described in the Gavin Wood paper. A list of keywords in Solidity are described in this file from its source, which includes “address”, “contract”, “event”, “mapping” and “wei” ( 1 Eth= 10^18 Wei). This list does not include “gas”, which is a mechanism described in Wood’s paper to combat abuse. Interestingly the paper says “The first example of utilising the proof-of-work as a strong economic signal to secure a currency was by Vishnumurthy et al [2003]”, aka the Karma paper.

The karma paper talks about solving a cryptographic puzzle as enabling one to join the network and be assigned a bank set: “the node certifies that it completed this computation by encrypting challenges provided by its bank-set nodes with its private key. Thus each node is assigned an id beyond its immediate control, and acquires a public-private key pair that can be used in later stages of the protocol without having to rely on a public-key infrastructure”. Crypto puzzles for diverseproblems have been proposed before, a survey and comparison is at

The DAO attack had 3 components, a hacker, a malicious contract and a vulnerable contract. The malicious contract is used to withdraw funds from the vulnerable contract so that it does not get a chance to decrement its balance. Oddly enough the gas mechanism which is supposed to limit computation did not kick in to stop this repeated remittance.

A few weeks before the DAO attack someone had pointed out to me that security of solidity was a bit of an open problem. My feeling was contracts should be layered above the value exchange mechanism, not built into it. Bitcoin based protocols with the simpler OP_RETURN semantics appeared more solid. Later around October’16 at an Ethereum meetup, Fred Ehrsam made the comment that most new projects would be using Ethereum instead of bitcoin. But Bitcoin meetups had more real-world use cases being discussed. The technical limitations exist, which are being addressed by forks such as SegWit2x this November.  Today saw a number of interesting proposals with Ethereum, including Dharma, DataWaller and BloomIDs. Security would be a continuing  concern with the expanding scope of such projects.

OCSP validation and OCSP stapling with letsencrypt

Online Certificate Status Protocol (OCSP) is a mechanism for browsers to check the validity of certificates presented by HTTPS websites. This guards against revoked certificates. This has been an issue for big websites, which had bad certs issued and had to be revoked. Google has stated its intent to begin distrusting Symantec certs in 2018. A counterpoint to Google appears in this interesting article which notes deficiencies in Chrome’s implementation of OCSP, and of privacy issue for the website visitors with OCSP checks.

Let’s look at what Mozilla is doing about this, as they have attempted to implement OCSCP correctly.

Telemetry indicates that fetching OCSP results is an important cause of slowness in the first TLS handshake. Firefox is, today, the only major browser still fetching OCSP by default for DV certificates.

In Bug 1361201 we tried reducing the OCSP timeout to 1 second (based on CERT_VALIDATION_HTTP_REQUEST_SUCCEEDED_TIME), but that seems to have caused only a 2% improvement in SSL_TIME_UNTIL_HANDSHAKE_FINISHED.

This bug is to disable OCSP fetching for DV certificates. OCSP fetching should remain enabled for EV certificates.

OCSP stapling will remain fully functional. We encourage everyone to use OCSP stapling.

So they are moving away from OCSP to OSCP stapling. From wikipedia, “OCSP stapling, formally known as the TLS Certificate Status Request extension, is an alternative approach to the Online Certificate Status Protocol(OCSP) for checking the revocation status of X.509 digital certificates.[1] It allows the presenter of a certificate to bear the resource cost involved in providing OCSP responses by appending (“stapling”) a time-stamped OCSP response signed by the CA to the initial TLS handshake, eliminating the need for clients to contact the CA”

How to setup OCSP stapling with letsencrypt. The CSR request can request OCSP_MUST_STAPLE in which case this option is built into the issued cert and some browsers will give an error unless the server has OCSP stapling enabled.

  1. Figure out which of the Let’s Encrypt certificates was used to sign your certificate.
  2. Download that certificate in PEM format.
  3. Point nginx to this file as the “trusted certificate”.
  4. In your nginx.conf file, add these directives to the same block that contains your other ssl directives:
    ssl_stapling on;
    ssl_stapling_verify on;
    ssl_trusted_certificate ssl/chain.pem;


Before I forget it’s name, this is a project that attempts to stabilize a cryptocurrency, so it does not have wild fluctuations.  Fiat currencies are actively managed to be stable and are also stabilized by being the dominant currency for labor negotiations which are slow moving changes. More on crypto stabilization in this post –!/main/articles/post-09-the-intelligent-cryptocurrency-investor .

Decentralized Identity Based on Blockchain

Sovrin project. Uses a Permissioned blockchain which allows it to do away with mining as an incentive and instead directly build a Distributed Ledger Technology which stores Distributed Identifiers (DIDs) and maps them to claims. Removal of mining frees up resources and increases network throughput. Interesting Key Management aspects, including revocation. Contrasts with Ethereum uPort – which is permissionless and public. Neat design, but will face adoption problem as it is unhitched from bitcoin/ethereum.–digital-identities-in-the-blockchain-era.pdf

DPKI – Distributed PKI. Attempts to do reduce the weakness of a centralized certificate authority as compromising that cert authority affects each of its issued certificates. This concept is built out and used in Sovrin.

Remme. Remember me. An approach to SSL based logins. Modifies SSL.
Used an EmerCoin implementation as mvp and Ethereum blockchain. EmerCoin: . Adoption problem here is change in behavior of each browser and mobile app.

Sidechains. Original proposal was to free up resources for when trust is established, to reuse blockchain technology and to establish a two-way peg between the sidechain and the blockchain.

Coco Framework.

HyperLedger – Linux based framework for developing blockchains software. Provides a DLT and uses Intel SGS extensions. (Intel+Microsoft+Linux foundation). Uses a replicated state machine model with each validating peer independently adding to its chain after reaching consensus on order of txns with other peers using Practical Byzantine Fault Tolerance or Proof of Elapsed Time. . Related –

A comparison link –


NVidia Tiny Linux Kernel and TrustZone

The NVidia Tiny Linux Kernel (TLK), is 23K lines of BSD licensed code, which supports multi-threading, IPC and thread scheduling and implements TrustZone features of a Trusted Execution Environment (TEE). It is based on the Little Kernel for embedded devices.

The TEE is an isolated environment that runs in parallel with an operating system, providing security. It is more secure than an OS and offers a higher level of functionality than a SE, using a hybrid approach that utilizes both hardware and software to protect data. Trusted applications running in a TEE have access to the full power of a device’s main processor and memory, while hardware isolation protects these from user installed apps running in a main operating system. Software and cryptographic isolation inside the TEE protect the trusted applications contained within from each other. A paper describing the separation with alternatives for virtualizing the TEE appeared at

TrustZone was developed by Trusted Foundations Software which was acquired by Gemalto. Giesecke & Devrient developed a rival implementation named Mobicore. In April 2012 ARM, Gemalto and Giesecke & Devrient combined their TrustZone portfolios into a joint venture Trustonic, which was the first to qualify a GlobalPlatform-compliant TEE product in 2013.

A comparison with other hardware based security technologies is found here. Whereas a TPM is exclusively for security functions and does not have access to the CPU,  the TEE does have such access.

Attacks against TrustZone on Android are described in this blackhat talk. With a TEE exploit,  “avc_has_perm” can be modified to bypass SELinux for Android. By the way, Access Vectors in SELinux are described in this wonderful link. “avc_has_perm” is a function to check the AccessVectors allows permission.

NVidia Volta GPU vs Google TPU

A Graphics Processing Unit (GPU) allows multiple hardware processors to act in parallel on a single array of data, allowing a divide and conquer approach to large computational tasks such as video frame rendering, image recognition, and various types of mathematical analysis including convolutional neural networks (CNNs). The GPU is typically placed on a larger chip which includes CPU(s) to direct data to the GPUs. This trend is making supercomputing tasks much cheaper than before .

Tesla_v100 is a System on Chip (SoC) which contains the Volta GPU which contains TensorCores, designed specifically for accelerating deep learning, by accelerating the matrix operation D = A*B+C, each input being a 4×4 matrix.  More on Volta at . It is helpful to read the architecture of the previous Pascal P100 chip which contains the GP100 GPU, described here – .  Background on why NVidia builds chips this way (SIMD < SIMT < SMT) is here – .

Volta GV100 GPU = 6 GraphicsProcessingClusters x  7 TextureProcessingCluster/GraphicsProcessingCluster x 2 StreamingMultiprocessor/TextureProcessingCluster x (64 FP32Units +64 INT32Units + 32 FP64Units +8 TensorCoreUnits +4 TextureUnits)

The FP32 cores are referred to as CUDA cores, which means 84×64 = 5376 CUDA cores per Volta GPU. The Tesla V100 which is the first product (SoC) to use the Volta GPU uses only 80 of the 84 SMs, or 80×64=5120 cores. The frequency of the chip is 1.455Ghz. The Fused-Multiply-Add (FMA) instruction does a multiplication and addition in a single instruction (a*b+c), resulting in 2 FP operations per instruction, giving a FLOPS of 1.455*2*5120=14.9 Tera FLOPs due to the CUDA cores alone. The TensorCores do a 3d Multiply-and-Add with 7x4x4+4×4=128 FP ops/cycle, for a total of 1.455*80*8*128 = 120TFLOPS for deep learning apps.

3D matrix multiplication3d_matrix_multiply

The Volta GPU uses a 12nm manufacturing process, down from 16nm for Pascal. For comparison the Jetson TX1 claims 1TFLOPS and the TX2 twice that (or same performance with half the power of TX1). The VOLTA will be available on Azure, AWS and platforms such as Facebook.  Several applications in Amazon. MS Cognitive toolkit will use it.

For comparison, the Google TPU runs at 700Mhz, and is manufactured with a 28nm process. Instead of FP operations, it uses quantization to integers and a systolic array approach to minimize the watts per matrix multiplication, and optimizes for neural network calculations instead of more general GPU operations.  The TPU uses a design based on an array of 256×256 multiply-accumulate (MAC) units, resulting in 92 Tera Integer ops/second.

Given that NVidia is targeting additional use cases such as computer vision and graphics rendering along with neural network use cases, this approach would not make sense.

Miscellaneous conference notes:

Nvidia DGX-1. “Personal Supercomputer” for $69000 was announced. This contains eight Tesla_v100 accelerators connected over NVLink.

Tesla. FHHL, Full Height, Half Length. Inferencing. Volta is ideal for inferencing, not just training. Also for data centers. Power and cooling use 40% of the datacenter.

As AI data floods the data centers, Volta can replace 500 CPUswith 33 GPUs.
Nvidia GPU cloud. Download the container of your choice. First hybrid deep learning cloud network. . Private beta extended to gtc attendees.

Containerization with GPU support. Host has the right NVidia driver. Docker from GPU cloud adapts to the host version. Single docker. Nvidiadocker tool to initialize the drivers.

Moores law comes to an end. Need AI at the edge, far from the data center. Need it to be compact and cheap.

Jetson board had a Tegra SoC chip which has 6cpus and a Pascal GPU.

AWS Device Shadows vs GE Digital Twins. Different focus. Availabaility+connectivity vs operational efficiency. Manufacturing perspective vs operational perspective. Locomotive may  be simulated when disconnected .

DeepInstinct analysed malware data using convolutional neural networks on GPUs, to better detect malware and its variations. – deep learning for time series data to detect anomalous conditions on sensors on the field such as pressure in a gas pipeline.

GANS applications to various problems – will be refined in next few years.

GeForce 960 video card. Older but popular card for gamers, used the Maxwell GPU, which is older than Pascal GPU.

Cooperative Groups in Cuda9. More on Cuda9.