Some useful lessons from Microsoft Hack

In July, Microsoft disclosed that a Chinese hacker group was able to access the mailboxes of some organizations. The attack used stolen signing keys. Recently, Microsoft published a post-mortem analysis of the incident and its remediation.
The analysis is an interesting read. There are many lessons and best practices. The following are my preferred ones.

Our investigation found that a consumer signing system crash in April of 2021 resulted in a snapshot of the crashed process (“crash dump”).  The crash dumps, which redact sensitive information, should not include the signing key.  In this case, a race condition allowed the key to be present in the crash dump (this issue has been corrected).  The key material’s presence in the crash dump was not detected by our systems (this issue has been corrected).

Memory dump is critical for security.  An attacker may find a key within the memory.  There are many techniques, such as entropy detection, brute force (This was the Muslix attack against AACS), pattern detection for PEM-encoded keys, etc.

Microsoft lists two impressive sets of security safeguards:

  1. Redact sensitive information from crash dumps before issuing them.
  2. Verification of the absence of key material (Like Github proposes when scanning code and binary)

Any secure software developer must know the risk associated with memory dump.  Clear keys in memory should be limited to its strict necessary time.  They should be erased or rewritten with nonce as soon as the code does not need them. 

Black Hat 2023 Day 2

  1. Keynote: Acting national cyber director  discusses the national cybersecurity strategy  and workforce efforts (K. WALDEN)

A new team at the White House of about 100 people dedicated to this task. No comment

The people ḍeciding which features require security reviews are not security experts. Can AI help?

The first issue is that engineering language is different than the normal language.   There is a lot of jargon and acronyms.  Thus, standard LLM may fail.

They explored several strategies of ML.

They used unsupervised training to define vector size (300 dimensions).  Then, they used convolution network with these vectors to make their decision.

The presentation is a good high-level introduction to basic techniques and the journey.

Missed 2% and false 5%.

The standard does not forbid JWE and JWS with asymmetric keys.  By changing the header, it was able to confuse the default behavior.

The second attack uses applications that use two different libraries, crypto and claims.  Each library handles different JSON parsing.   It is then possible to create inconsistency.

The third attack is a DOS by putting the PBKDF2 iteration value extremely high.

My Conclusion

As a developer, ensure at the validation the use of limited known algorithms and parameters.

ChatGPT demonstrates the vulnerability of humans to being bad at testing

When demonstrating a model, are we sure they are not using trained data as input to the demonstration.  This trick ensures PREDICTABILITY.

Train yourself in ML as you will need it.

Very manual methodology using traditional reverse engineering techniques

Laion5B is THE dataset of 5T images.   It is a list of URLs.  But registered domains expire and can be bought.  Thus, they may be poisoned.  It is not a targeted attack, as the attacker does not control who uses it.

0.01% may be sufficient to poison.

It shows the risk of untrusted Internet data.  Curated data may be untrustworthy.

The attack is to use Java polymorphism to override the normal deserialization.  The purpose is to detect this chain.

Their approach uses tainted data analysis and then fuzz.

Black Hat 2023 Day 1

  1. Introduction (J. MOSS)

Jeff MOSS (Founder of DefCon and Black Hat) highlighted some points:

  • AI is about using predictions. 
  • AI brings new issues with Intellectual Properties.   He cited the example of Zoom™ that just decided that all our interactions could be used for their ML training.
  • Need for authentic data.

The current ML models are insecure, but people trust them.  Labs had LLMs available for many years but kept them.  With OpenAI going public, it started the race.

She presents trends for enterprise:

  • Enterprise’s answer to ChatGPT is Machine Learning as a Service (MLaaS).  But these services are not secure.
  • The next generation should be multi-modal models (using audio, image, video, text…).  More potent than monomodal ones such as LLMs.
  • Autonomous agent mixes the data collection of LLM and takes decisions and actions.  These models will need secure authorized access to enterprise data.  Unfortunately, their actions are non-deterministic.
  • Data security for training is critical.  It is even more challenging when using real-time data.

She pointed to an interesting paper about poisoning multi-modal data via image or sound.

Often, the power LED is more or less at the entry of the power supply circuit.  Thus, intensity is correlated to the consumption.

They recorded only the image of the LED to see the effect of the rolling shutter.  Thus, they increase the sampling rate on the LED with the same video frequency.  This is a clever, “cheap” trick.

To attack ECDSA, they used the Minerva attack (2020)

Conclusion: They turned timing attacks into a power attack.  The attacks need two conditions:

  1. The implementation must be prone to some side-channel timing attack.
  2. The target must have a power LED in a simple setting, such as a smart card reader, or USB speakers. 

Despite these limitations, it is clever.

Once more, users trust AI blindly.

The global environment is complex and extends further than ML code.

All traditional security issues are still present, such as dependency injection.

The current systems are not secure against adversarial examples.  They may not even present the same robustness of all data points.

Explainability is insufficient if it is not trustworthy.  Furthermore, the fairness and trustworthiness of the entity using the explanation are essential.

The Multi-Party Computation (MPC) Lindel17 specifies that all further interactions should be blocked when a finalized signature fails.  In other words, the wallet should be blocked.  They found a way to exfiltrate the part key if the wallet is not blocked (it was the case for several wallets)

In the case of GG18 and GG20, they gained the full key by zeroing the ZKP using the CRT (Chinese Remainder Theorem) and choosing a small factor prime.

Conclusion: Adding ZKP in protocols to ensure that some design hypotheses are enforced.

They created H26forge to create vulnerable H264 content.  They attack the semantics out of its specified range.  Decoders may not test all of them.  The tool helps with handling the creation of forged H264. 


This may be devastating if combined with fuzzing.

Enforce the limits in the code.

If the EKU (extended key use) is not properly verified for its purpose, bingo.

Some tested implementations failed the verification.  The speaker forged the signing tools to accept domain-validated certificates for signing code.

Politically correct but not really informative.

Breaching the Samsung S9 Keystore

Most Android devices implement an Android Hardware-backed Keystore.  The Rich Execution Environment (REE) applications, i.e., the unsecure ones, use a hardware root of trust and an application in the Trusted Execution Environment (TEE).  Usually, as all the cryptographic operations occur only in the trusted part, these keys should be safe.

Three researchers from the Tel-Aviv university demonstrated that it is not necessarily the case.  ARM’s TrustZone is one of the most used TEEs.  Each vendor must write its own Trusted Application (TA) that executes in the TrustZone for its key store.  The researchers reverse-engineered the Samsung implementation for S8, S9, S20, and S21.  They succeeded in breaching the keys protected by the key store.

The breach is not due to a vulnerability in TrustZone.  It is due to design errors in the TA.

When REE requests to generate a new key, the TA returns a wrapped key, i.e., a key encrypted with a key stored in the root of trust.  In a simplified explanation, the wrapped key is the newly generated key AES-CGM-encrypted with an IV provided by the REE application and a Hardware-Derived Key (HDK) derived from some information supplied by the REE application and the hardware root of trust key.

 In other words, the REE application provides the IV and some data that generate the HDK.  AES-CGM is a stream cipher (uses AES CTR), and thus it is sensitive to IV reuse.  With a streamcipher, you must never reuse an IV with the same key.  Else, it is easy to retrieve the encrypted message with a known ciphertext.  In this case, the attacker has access to the IV used to encrypt the wrapped key and can provide the same `seed` for generating the HDK.   Game over!

In S20 and S21, the key derivation function adds some randomness for each new HDK.  The attacker cannot anymore generate the same HDK.  Unfortunately, the S20 andS21 TA contains the old derivation function.  The researchers found a way to downgrade to the S9 HDK.  Once more, game over!


  1. Never reuse an IV with a streamcipher.  Do not trust the user to generate a new IV, do it yourself.
  2. A Trusted Execution Environment does not protect from a weak/wicked “trusted” application. 
  3. If not necessary, remove all unused software from the implementation.  You reduce the attack surface.


A. Shakevsky, E. Ronen, and A. Wool, “Trust Dies in Darkness: Shedding Light on Samsung’s TrustZone Keymaster Design,” 208, 2022.  Available:

Black Hat 2021: my preferred talks

Last week, I attended Black Hat 2021. It was a hybrid conference, i.e., both on-site and virtual. As a consequence, there were only four concurrent “physical’ talks at any moment. The number of attendees was far lower than in 2019. I attended the physical ones exclusively with a focus on hacking.

I enjoyed the most the two following talks

Breaking the Isolation: Cross-Account AWS Vulnerabilities by Shir Tamari and Ami Luttwak
They explored the AWS cross services such as CloudTrail or the Serverless Repository. Such services allow to store some data in the same location for several services or read data from the same location for several services. They discovered that the security policy configuration did not define the referenced accounts. Thus, it was possible to use CloudTrail to store files in an S3 bucket that you did not control.
AWS has fixed the issue. Unfortunately, it is up to the customer to update the policies correspondingly; else, the holes are still present.
Fixing a Memory Forensics Blind Spot: Linux Kernel Tracing by Andrew Case and Golden Richard
The ePBF is a programming language that makes access to the Linux kernel tracing easy. The tracing system is mighty. It allows to read registers, hook subsystem calls, etc. From the userland!! Powerful but nasty.
They presented some extensions of their open-source tools to list the hooked calls and other stealthy operations.
I was not aware of ePBF. It opened my eyes and scared me. An earlier talk With Friends Like eBPF, Who Needs Enemies? The authors presented a rootkit based on ePBF. Unfortunately, I did not attend this talk. Would I have known ePBF, I would have attended it. It seems that there were three other ePBF-based talks at DefCon 2021.

In the coming weeks, I will listen to some virtual talks and report the ones I enjoyed.

The fall of Titans?

Two French security researchers, Victor Lomne and Thomas Roche, published in January an impressive 55-page report.  The report describes a successful Electro-Magnetic side-channel attack on Google’s Titan security key.  They succeeded in extracting the ECDSA private key.

Titan security key is a FIDO U2F compliant key also known as Google authenticator.  It is functionally similar to Yubikeys.  Its purpose is to serve as a physical token for Two-Factor Authentication (2FA).

Mounting side-channel attacks on secure components like smart cards is “common.”  It usually assumes the attacker has samples to analyze and that the attacker can store arbitrary known secrets in the samples.  This knowledge provides some reference points during the attack.  Once the attack is fine-tuned with the samples using a known secret, it is possible to extract the target’s secret. Unfortunately, this is not true in this specific use case.  When registering, the token generates its ECDSA key pair.  The private key never leaves the token.  It is why it is not possible to back up such tokens.  Thus, it is possible to purchase Titan tokens, but not to feed an arbitrary key pair.  The researchers used an interesting methodology to overcome this issue.

They first identified the secure component used by Titan. They removed the plastic cover and identified NXP A7005.  They found out that some JavaCards have similar characteristics to the NXP A7005.  Thus, they used JavaCards using NXP P5x chips.

Using a 500µm coil with 10µm precision micromanipulators, they measured the EM signature of the ECDSA signing for both Titan and the JavaCard.  The comparison of the two EM signatures confirmed that they used the same implementation.  Thus, they concentrated their effort on the Javacard to design the exploit.  They reverse-engineered the implementation using the EM traces to guess the calculations. They discovered a sensitive leakage and could mount a complex side-channel attack.  The document details the complexity of the attack.  With 4,000 sampled signatures for 2TB of data, they succeeded in extracting the key that they fed to the smart card.

Then, they implemented the same attack on the Titan chip.  They increased the number of samples to 6,000 for 3TB of data.   They succeeded in extracting the private key.

How devastating is this attack?

  • The specialized equipment is about 10K€ (about $12K). The needed skill set is high.  On the  Common Criteria (CC) scale, it has a rating of 27 corresponding to attackers with moderate attack potential.  The corresponding chips are old and are not any more covered by CC certificates.
  • The attack requires the attacker to get the Titan key for several hours to collect the 6,000 samples.  It is not possible to clone it.
  • The attack requires opening the plastic casing.  The operation seems destructive.  For stealthiness, the attacker must be able to repackage the chip in a legitimate case.
  • The attacker needs to return the “borrowed” recased key to the legitimate owner. Else this owner may detect the loss and block the access.
  • This attack impacts not only the Titan token but a long list of components.

Thus, we may forecast that such attack would be efficient only against very high-profile targets.


The attack is an impressive piece of work.  Reading the document gives an overview of the issues a side-channel attack requires to solve. It is extremely interesting.

Diversity of implementation across different products is a costly but secure option.

Continue to use your 2FA tokens.  It is more secure than not using them.  If you lost your 2FA token, change your accounts to use a new one as soon as possible (which should be the case, independently of this attack).

Use 2FA tokens as much as possible.


Lomne, Victor, and Thomas Roche. “A Side Journey to Titan.” NinjaLab, January 7, 2021.

VoltPillager: a new attack on SGX

In the past, several voltage-based fault injection attacks on Intel SGX were successful.  The attacks used software commands to control the voltage of the CPU.  Intel’s mitigation was to disable the access to these commands at the BIOS level.

A team of researchers from the University of Birmingham succeeded in performing the same type of attacks but via external hardware, therefore bypassing the mitigation.  They coined the attack: VoltPillager [1].

On the motherboard, the CPU controls its voltage by sending commands to a programmable Voltage Controller using an undocumented three-wire serial bus: Software Voltage Identification (SVID).  The bus is somewhat similar to I2C.  The researchers reverse-engineered its protocol.  SVID has no protection.  Furthermore, the bus uses open-drain outputs; thus, it is “easy” to piggy-back on SVID.  Open-drain outputs require resistors to pull the signal to `1’; therefore, the connection is accessible easily and can use jumpers or soldering. 

They used a $20 Teensy 4.0 microcontroller [2].  To synchronize the Teensy microcontroller’s commands with the target’s CPU activity, the team used an RS232 UART rather than the USB port.  RS232 has minimal jitter compared to USB enabling a more accurate synchronization.  Time accuracy is critical in fault injection attacks.  The VoltPillager hardware allows issuing commands that program the shape of the undervolting waveform accurately.  Hardware-driven undervolting is more precise and accurate than equivalent software-driven fault-injection attacks.

They succeeded in reproducing the exploits disclosed by the Plundervolt attack [3]. Due to its increased accuracy, VoltPillager requested 50 times fewer iterations than Plundervolt.  Furthermore, the team discovered a new class of attacks.  It seems that undervolting briefly delays memory writes to the cache.  This potential delay opens many opportunities for cache attacks.

The bad news is that VoltPillager bypasses the mitigation introduced by Intel against the Plundervolt-like attacks.  VoltPillager attacks the Voltage Controller directly.  As explained in the paper, protecting the bus would not solve the issue because a determined attacker could directly drive the Pulse Wave Modulation that manages the actual voltage.  The most efficient mitigation would use techniques inspired by the smart card world, such as the hardware detector of voltage glitches or redundant critical code.

Of course, VoltPillager requests physical access to the CPU.  Intel’s answer to VoltPillager is

“… opening the case and tampering of internal hardware to compromise SGX is out of scope for SGX threat model.” 

This assumption of the threat model may be valid in many scenarios.  Unfortunately, tampering with hardware is in the scope of the content protection threat model.

A thrilling paper that reminds us that software runs on hardware and hardware is difficult to secure.

[1]          Z. Chen, G. Vasilakis, K. Murdock, E. Dean, D. Oswald, and F. Garcia, “VoltPillager: Hardware-based fault injection attacks against Intel SGX Enclaves using the SVID voltage scaling interface.” Nov. 2020, [Online]. Available:

[2]          “Teensy USB Development Board.”

[3]          “Plundervolt.”