How do hackers guess passwords without the help of AI?
In the age of cybersecurity, most organizations emphasize password complexity rules or sophisticated solutions like artificial intelligence for account protection. However, attackers don’t necessarily need AI to guess passwords; classic methods, well-understood and adapted to the context, remain extremely effective.
Why password guessing attacks work without AI
Contrary to general perception, most password guessing attacks do not involve artificial intelligence models or advanced machine learning techniques. Instead, attackers use password lists generated based on publicly available language and information about the targeted organization.
This approach exploits a simple fact: users tend to create passwords that are easy to remember. Instead of going for completely random combinations of characters, they include familiar terms in their passwords. For example, company names, products, locations, or other details associated with the organization.
What are contextualized password lists (targeted wordlists)
Context-adapted password lists are generated by collecting words and phrases from the public sources of the targeted organization. These include websites, blogs, service description pages, etc. The gathered information is then transformed into possible password combinations.
A very popular tool for this task is CeWL (Custom Word List Generator), an open-source crawler widely used by pentesters and attackers alike. CeWL extracts terms from web pages, then creates a word list that reflects the organization’s specific language.
These “contextual” terms usually include:
- Company or organization name
- Services, products, or internal acronyms
- Location or project names
- Industry terms relevant to the domain.
The resulting list may seem unusual for a “generic” cyberattack, but for a targeted attack, it is particularly effective because it accurately reflects the language used within the organization.
How passwords are practically deduced
A simple list of words extracted from a site is not enough for an effective attack. This “database” is then subjected to a “treatment” with permutations and combinations to generate passwords. Examples of transformations include:
- Adding numbers to the end of words (e.g., Company123)
- Capitalization changes (company, Company, COMPANY)
- Adding symbols like !, @, #
- Combining multiple terms into a single string.
Tools like Hashcat then allow these mutation rules to be used on a large scale, making it possible to efficiently test millions of combinations based on the same thematic list.
Why classic complexity rules are not enough
Most password approval policies still require:
- At least one uppercase character
- At least one digit
- At least one special character.
The problem is that these rules are easy to satisfy, and even passwords constructed from sensitive terms, but superficially modified, can meet complexity requirements. For example, the password
NumeCompanie123! is long and meets all technical criteria, but remains extremely predictable if the attacker has a knowledge base about the respective organization.
According to analyses performed on billions of compromised passwords, such choices are frequent and easy to exploit.
How to defend yourself effectively
1. Avoid passwords based on contextual language
Policy rules should go beyond basic complexity requirements and prevent the use of:
- Company or system name
- Internal project names
- Industry terms
- Clear variables or those easily associated with the organization.
2. Block already compromised passwords
A modern practice is to block passwords already found in known data breaches. This prevents the reuse of the same compromised passwords, even if they meet complexity requirements.
3. Create long and real passwords (passphrase)
Long passphrase passwords (15+ characters), formed from unrelated words, are considerably harder to guess even by targeted attacks.
4. Enable multi-factor authentication (MFA)
MFA does not prevent a password from being compromised, but it significantly reduces the risk of an attacker using the compromised password to gain access.
Conclusion
Attacks based on password deduction are not always reliant on advanced AI algorithms; sometimes the most effective methods are simple but well-adapted to the context. By generating word lists specific to an organization’s language and terminology, attackers can guess passwords with a much higher success rate than if they only used generic password lists.
To protect yourself correctly, it is essential to implement security policies aimed at preventing the use of predictable passwords, blocking compromised passwords, and applying multi-factor authentication wherever possible.
Source: bleepingcomputer.com
