free eBooks! AI & Codebreaking

"Sealed" Train harrumph

Eric Engle

May 27, 2024

Leveraging LLM AI for Cryptography, free eBooks, and news in this issue.

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外星人!

A few good men:

somewhere in Russia there is another Lenin, another Mao, another Jefferson, another Kant. You can't find any of them. You can leave out the trails of tools, tricks of the trade, things they need to advance their purpose. It is a bit like a violent scavenger hunt, with many riddles along the way. Kit out the right men and you get the outcome you desire. The converse is also true.

I can tell you, there is indeed such a device.

学中文,解密!

CRIBS, CRYPTOGRAPHY, AND AI

Identification and generation of cribs—segments of known plaintext that correspond to sections of ciphertext—stands out as a particularly potent method for key recovery. In our modern era, the advent of large language models (LLMs) and the wealth of open-source materials have revolutionized this endeavor, providing unprecedented tools for extracting high-value plaintext likely to appear in a given ciphertext.In this essay, I shall explore the fascinating realm of finding and generating cribs, known plain text, and recovering cipher keys using large language models and open source materials. This approach, inspired by the pioneering work of Alan Turing, leverages the power of machine learning and natural language processing to uncover potential high-value plain texts that may occur in a given cipher text.

The Concept of Cribs and Known Plain Text

A crib, in cryptographic terminology, refers to a segment of known plaintext that the cryptanalyst suspects or knows will appear in the ciphertext. In other words, a crib is a piece of known plain text that corresponds to a specific portion of the encrypted text. The identification of such cribs can greatly facilitate the decryption process, as it allows the analyst to make educated guesses about the corresponding sections of the key.

The discovery of a crib can be a crucial breakthrough in the cryptanalysis process, as it provides a foothold for further analysis and potentially leads to the recovery of the cipher key.

Consider the classic example from World War II, where cryptanalysts at Bletchley Park leveraged the predictable content of German weather reports to find cribs within the Enigma-encrypted messages. The recurring phrase "Wettervorhersage" (weather forecast) served as a vital crib, enabling the decryption of vast amounts of German military communications.

Traditionally, cribs were obtained through careful analysis of the encrypted text, often relying on frequency analysis, pattern recognition, and educated guesswork. However, with the advent of large language models and open source materials, we can now employ a more systematic and efficient approach to finding cribs.

AI Leveraging Large Language Models

Large language models, such as those based on transformer architectures, have demonstrated remarkable capabilities in natural language processing tasks, including text generation, language translation, and text classification. By fine-tuning these models on open source materials, such as news reports, foreign policy statements, and other publicly available texts, we can create a powerful tool for generating potential cribs. These models, trained on vast corpora of text, possess an uncanny ability to predict and generate text that is contextually and semantically

The process begins by collecting a large corpus of open source materials, which are then used to train a language model. This model is designed to learn the patterns, structures, and relationships within the language, allowing it to generate text that is coherent and contextually relevant. By feeding the model a prompt or a set of keywords related to the cipher text, we can generate a set of potential cribs that may correspond to the encrypted text.

For example, suppose we are attempting to cryptanalyze a cipher text related to a recent diplomatic incident between two nations. We can collect a corpus of news reports, official statements, foreign policy statements, and diplomatic communiqués related to the incident, and use this corpus to train a language model. By feeding the model keywords such as "diplomatic incident," "nation A," and "nation B,". Therefrom, a large language model can generate plausible plaintext candidates that are likely to be contained within the encrypted message, generating a set of potential cribs that may correspond to cipher text. These cribs can then be used as a starting point for further analysis, potentially leading to the recovery of the cipher key.

To illustrate this approach in detail, let´s consider a hypothetical example. Suppose we have a cipher text that reads:

`GUR PENMLXO YTWQZXRL VF ZL FRPERG CBFG`

This ciphered communication is suspected to pertain to a diplomatic negotiation between two countries. Open-source materials reveal that recent talks have centered around trade agreements. Utilizing Using a large language model trained on a corpus of diplomatic communiqués, we generate a set of potential cribs related to the keywords "diplomatic incident" and "nation A." The generated cribs read:

1. "trade agreement"

2. "negotiation terms"

3. "economic partnership"

4. "import tariffs"

5. "export quotas"

6. "the foreign minister"

By comparing the crib to the cipher text, we notice that the first few words of the crib ("THE FOREIGN MINISTER") correspond to a portion of the cipher text (`GUR PENMLXO YTWQZXRL`). This suggests that the crib may be a valid match. By matching these generated cribs against the ciphertext, we can identify segments where the plaintext aligns with the cipher. This process, and further analysis, iteratively refined, can significantly

By leveraging large language models and open source materials, we can automate the process of finding cribs, reducing the time and effort required for cryptanalysis. Furthermore, this approach can be applied to a wide range of cipher texts, including those used in secure communication protocols, encrypted files, and other cryptographic systems.

Extracting High-Value Plain Text from Open-Source Materials

Open-source materials provide provide a plethora of high-value plaintexts. News reports, foreign policy statements, and even social media posts can yield phrases and terminology that are likely candidates for cribs. For instance, during a period of heightened military tension, military communiques are likely to contain specific terminology about troop movements, strategic objectives, and operational codes. By systematically extracting and analyzing these terms from open-access sources, we can compile a comprehensive database of potential cribs.

Consider another example: a series of encrypted financial communications intercepted during an investigation into corporate espionage. By mining financial news reports and market analysis, we can identify terms and phrases such as:

1. "quarterly earnings"

2. "merger and acquisition"

3. "market share"

4. "fiscal year"

5. "investment strategy"

These terms, when matched against the ciphertext, provide valuable points of reference for the cryptanalyst, enabling more precise and targeted key recovery efforts.

Implications and Inferences

The integration of large language models and open-source materials into the cryptanalytic process heralds a new era of decryption capability. The implications of this are manifold:

1. Increased Efficiency: The ability to quickly generate and validate potential cribs reduces the time and computational resources required for decryption.

2. Enhanced Accuracy: Leveraging contextual knowledge from open-source materials increases the likelihood of identifying correct plaintext segments, thus improving the overall accuracy of key recovery.

3. Scalability: The automated nature of large language models allows for the processing of vast amounts of data, making it feasible to tackle a larger volume of encrypted communications simultaneously.

In conclusion, the use of large language models and open source materials offers a powerful new approach to finding and generating cribs, known plain text, and recovering cipher keys. By intelligently generating and identifying cribs, we can significantly enhance our ability to recover cipher keys and decrypt communications. By harnessing machine learning and natural language processing, we can unlock the secrets hidden within encrypted texts. As this technology continues to evolve, it promises to unlock new frontiers in the analysis and understanding of encrypted data, reaffirming the timeless adage that knowledge, when aptly applied, is indeed power.

"Can machines think?" Whatever the answer, in cryptanalysis, machines may just hold the key to unlocking the secrets of the encrypted text.

The Uncertainty Principle: A Fundamental Obstacle to Quantum Cryptography

The quantum uncertainty principle holds serious implications for on the feasibility of quantum computing. The notion that we can harness the power of quanta to perform calculations and process information is, in my opinion, a tantalizing dream that is unlikely to materialize. The uncertainty principle, a fundamental aspect of quantum mechanics, poses a significant obstacle to the development of reliable and efficient quantum computers.

The uncertainty principle, first formulated by Werner Heisenberg, states that it is impossible to know certain properties of a quantum system, such as position and momentum, simultaneously with infinite precision. This principle is a direct consequence of the wave-particle duality of quantum objects, which exhibit both wave-like and particle-like behavior. The act of measurement itself introduces uncertainty, making it impossible to predict the outcome of a measurement with certainty.

Now, let us consider the implications of this principle on the development of quantum computers. In a classical computer, bits are used to represent information, and these bits can be in one of two states, 0 or 1. However, in a quantum computer, the fundamental unit of information is the qubit, which can exist in a superposition of both 0 and 1 states simultaneously. This property allows for the possibility of performing multiple calculations simultaneously, making quantum computers potentially much faster than their classical counterparts.

However, the uncertainty principle throws a wrench into this machinery. If we attempt to use quanta as decision gates, we would need to measure their state to determine the outcome of a calculation. But, as the uncertainty principle dictates, the act of measurement itself introduces uncertainty, making it impossible to predict the outcome of the measurement with certainty. This means that the qubits, which are the fundamental building blocks of a quantum computer, would be inherently unpredictable, rendering the entire system unreliable.

To illustrate this point, let us consider a simple example. Suppose we have a qubit that is in a superposition of both 0 and 1 states, and we want to use it to perform a calculation. We would need to measure the state of the qubit to determine the outcome of the calculation. However, due to the uncertainty principle, the act of measurement would introduce uncertainty, causing the qubit to collapse into either a 0 or 1 state randomly. This means that the outcome of the calculation would be unpredictable, making it impossible to rely on the result.

Furthermore, the uncertainty principle also implies that the qubits would be prone to errors, making it difficult to maintain the fragile quantum states required for quantum computing. The slightest disturbance, such as a photon interacting with the qubit, could cause the qubit to collapse into an incorrect state, leading to errors in the calculation.

In addition, the uncertainty principle also raises questions about the scalability of quantum computers. As the number of qubits increases, the complexity of the system grows exponentially, making it increasingly difficult to maintain control over the qubits. The uncertainty principle would introduce an additional layer of complexity, making it even more challenging to build a reliable and efficient quantum computer.

In conclusion, the uncertainty principle poses a significant obstacle to the development of quantum computers. The unpredictability of qubits, introduced by the uncertainty principle, makes it impossible to rely on the outcome of calculations, rendering the entire system unreliable. While the idea of quantum computing is intriguing, I fear that it may remain a pipe dream, forever elusive due to the fundamental limitations imposed by the uncertainty principle.

The Armourer merely equips, he does not command. . .