Chosen-Prefix Collisions for MD5 and Applications

We present a novel, automated way to find differential paths for MD5. Its main application is in the construction of \emph{chosen-prefix collisions}. We have shown how, at an approximate expected cost of $2^{39}$ calls to the MD5 compression function, for any two chosen message prefixes $P$ and $P'$, suffixes $S$ and $S'$ can be constructed such that the concatenated values $P\|S$ and $P'\|S'$ collide under MD5. The practical attack potential of this construction of chosen-prefix collisions is of greater concern than the MD5-collisions that were published before. This is illustrated by a pair of MD5-based X.509 certificates one of which was signed by a commercial Certification Authority (CA) as a legitimate website certificate, while the other one is a certificate for a rogue CA that is entirely under our control (cf.\ \url{http://www.win.tue.nl/hashclash/rogue-ca/}). Other examples, such as MD5-colliding executables, are presented as well. More details can be found on \url{http://www.win.tue.nl/hashclash/ChosenPrefixCollisions/}

Reverse-Engineering of the Cryptanalytic Attack Used in the Flame Super-Malware

In May 2012, a highly advanced malware for espionage dubbed Flame was found targeting the Middle-East. As it turned out, it used a forged signature to infect Windows machines by MITM-ing Windows Update. Using counter-cryptanalysis, Stevens found that the forged signature was made possible by a chosen-prefix attack on MD5 \cite{DBLP:conf/crypto/Stevens13}. He uncovered some details that prove that this attack differs from collision attacks in the public literature, yet many questions about techniques and complexity remained unanswered. In this paper, we demonstrate that significantly more information can be deduced from the example collision. Namely, that these details are actually sufficient to reconstruct the collision attack to a great extent using some weak logical assumptions. In particular, we contribute an analysis of the differential path family for each of the four near-collision blocks, the chaining value differences elimination procedure and a complexity analysis of the near-collision block attacks and the associated birthday search for various parameter choices. Furthermore, we were able to prove a lower-bound for the attack's complexity. This reverse-engineering of a non-academic cryptanalytic attack exploited in the real world seems to be without precedent. As it allegedly was developed by some nation-state(s), we discuss potential insights to their cryptanalytic knowledge and capabilities

Reverse-Engineering of the Cryptanalytic Attack Used in the Flame Super-Malware

In May 2012, a highly advanced malware for espionage dubbed Flame was found targeting the Middle-East. As it turned out, it used a forged signature to infect Windows machines by MITM-ing Windows Update. Using counter-cryptanalysis, Stevens found that the forged signature was made possible by a chosen-prefix attack on MD5 \cite{DBLP:conf/crypto/Stevens13}. He uncovered some details that prove that this attack differs from collision attacks in the public literature, yet many questions about techniques and complexity remained unanswered. In this paper, we demonstrate that significantly more information can be deduced from the example collision. Namely, that these details are actually sufficient to reconstruct the collision attack to a great extent using some weak logical assumptions. In particular, we contribute an analysis of the differential path family for each of the four near-collision blocks, the chaining value differences elimination procedure and a complexity analysis of the near-collision block attacks and the associated birthday search for various parameter choices. Furthermore, we were able to prove a lower-bound for the attack's complexity. This reverse-engineering of a non-academic cryptanalytic attack exploited in the real world seems to be without precedent. As it allegedly was developed by some nation-state(s) \cite{WashingtonPost_Flame,kaspersky_flame,crysis_flame}, we discuss potential insights to their cryptanalytic knowledge and capabilities

SHA-1 is a Shambles: First Chosen-Prefix Collision on SHA-1 and Application to the PGP Web of Trust

International audienceThe SHA-1 hash function was designed in 1995 and has been widely used during two decades. A theoretical collision attack was first proposed in 2004 [29], but due to its high complexity it was only implemented in practice in 2017, using a large GPU cluster [23]. More recently, an almost practical chosen-prefix collision attack against SHA-1 has been proposed [12]. This more powerful attack allows to build colliding messages with two arbitrary prefixes, which is much more threatening for real protocols. In this paper, we report the first practical implementation of this attack, and its impact on real-world security with a PGP/GnuPG impersonation attack. We managed to significantly reduce the complexity of collision attacks against SHA-1: on an Nvidia GTX 970, identical-prefix collisions can now be computed with a complexity (expressed in terms of SHA-1 equivalents on this GPU) of 2 61.2 rather than 2 64.7 , and chosen-prefix collisions with a complexity of 2 63.4 rather than 2 67.1. When renting cheap GPUs, this translates to a cost of US$11k for a collision, and US$ 45k for a chosen-prefix collision, within the means of academic researchers. Our actual attack required two months of computations using 900 Nvidia GTX 1060 GPUs (we paid US$ 75k because GPU prices were higher, and we wasted some time preparing the attack). Therefore, the same attacks that have been practical on MD5 since 2009 are now practical on SHA-1. In particular, chosen-prefix collisions can break signature schemes and handshake security in secure channel protocols (TLS, SSH), if generated extremely quickly. We strongly advise to remove SHA-1 from those type of applications as soon as possible. We exemplify our cryptanalysis by creating a pair of PGP/GnuPG keys with different identities, but colliding SHA-1 certificates. A SHA-1 certification of the first key can therefore be transferred to the second key, leading to an impersonation attack. This proves that SHA-1 signatures now offer virtually no security in practice. The legacy branch of GnuPG still uses SHA-1 by default for identity certifications, but after notifying the authors, the modern branch now rejects SHA-1 signatures (the issue is tracked as CVE-2019-14855)

Collision Attack on 5 Rounds of Grøstl

In this article, we describe a novel collision attack for up to 5 rounds of the Grøstl hash function. This significantly improves upon the best previously published results on 3 rounds. By using a new type of differential trail spanning over more than one message block we are able to construct collisions for Grøstl on 4 and 5 rounds with complexity of $2^{67}$ and $2^{120}$, respectively. Both attacks need $2^{64}$ memory. Due to the generic nature of our attack we can even construct meaningful collisions in the chosen-prefix setting with the same attack complexity

Deploying a New Hash Algorithm

The strength of hash functions such as MD5 and SHA-1 has been called into question as a result of recent discoveries. Regardless of whether or not it is necessary to move away from those now, it is clear that it will be necessary to do so in the not-too-distant future. This poses a number of challenges, especially for certificate-based protocols. We analyze a number of protocols, including S/MIME and TLS. All require protocol or implementation changes. We explain the necessary changes, show how the conversion can be done, and list what measures should be taken immediately

Chosen-prefix collisions for MD5 and applications

We present a novel, automated way to find differential paths for MD5. Its main application is in the construction of chosen-prefix collisions. We have shown how, at an approximate expected cost of 239 calls to the MD5 compression function, for any two chosen message prefixes P and P', suffixes S and S' can be constructed such that the concatenated values P||S and P'||S' collide under MD5. The practical attack potential of this construction of chosen-prefix collisions is of greater concern than the MD5-collisions that were published before. This is illustrated by a pair of MD5-based X.509 certificates one of which was signed by a commercial Certification Authority (CA) as a legitimate website certificate, while the other one is a certificate for a rogue CA that is entirely under our control (cf. http://www.win.tue.nl/hashclash/rogue-ca/). Other examples, such as MD5-colliding executables, are presented as well. More details can be found on http://www.win.tue.nl/hashclash/ChosenPrefixCollisions/

Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained Models

Large-scale pre-trained models (PTMs) such as BERT and GPT have achieved great success in diverse fields. The typical paradigm is to pre-train a big deep learning model on large-scale data sets, and then fine-tune the model on small task-specific data sets for downstream tasks. Although PTMs have rapidly progressed with wide real-world applications, they also pose significant risks of potential attacks. Existing backdoor attacks or data poisoning methods often build up the assumption that the attacker invades the computers of victims or accesses the target data, which is challenging in real-world scenarios. In this paper, we propose a novel framework for an invisible attack on PTMs with enhanced MD5 collision. The key idea is to generate two equal-size models with the same MD5 checksum by leveraging the MD5 chosen-prefix collision. Afterwards, the two ``same" models will be deployed on public websites to induce victims to download the poisoned model. Unlike conventional attacks on deep learning models, this new attack is flexible, covert, and model-independent. Additionally, we propose a simple defensive strategy for recognizing the MD5 chosen-prefix collision and provide a theoretical justification for its feasibility. We extensively validate the effectiveness and stealthiness of our proposed attack and defensive method on different models and data sets