Ethical Hacking News
In a bid to counter the growing threat of evasion attacks on large language models (LLMs), Germany's Federal Office for Information Security (BSI) has issued a new publication outlining various measures to secure AI systems. The document highlights the rising trend of evasion attacks and provides recommendations for implementing layered safeguards and continuous monitoring to address these risks. By adopting proactive measures, developers and IT managers can help reduce potential harm from evasion attacks on LLMs.
The German Federal Office for Information Security (BSI) has issued a publication to help developers and IT managers secure their AI systems against evasion attacks on large language models (LLMs). Evasion attacks are a growing concern, where malicious inputs aim to subvert or bypass model safeguards. The BSI recommends countermeasures such as secure system prompts, malicious content filtering, and explicit user confirmation before execution. Organizations must adopt layered safeguards and continuous monitoring to address the risks of evasion attacks on LLMs.
Germany’s Federal Office for Information Security (BSI) has recently issued a publication aimed at developers, IT managers in companies and public authorities who use pre-trained models such as OpenAI's GPT. The document specifically targets those individuals who utilize large language models (LLMs) to tackle the growing threat of evasion attacks on these systems.
Evasion attacks are a significant concern for AI systems based on LLMs, where malicious inputs are designed to subvert or bypass model safeguards. These types of attacks have been increasingly prevalent in recent times and can cause substantial damage if left unchecked. The BSI has acknowledged the rising trend of evasion attacks and is urging developers and IT managers to take proactive measures to secure their AI systems.
The new guidance document outlines various countermeasures that can be implemented to mitigate the risks associated with evasion attacks on LLMs. These include secure system prompts, malicious content filtering, and requiring explicit user confirmation before execution. The BSI has also provided a practical checklist that helps integrate these defenses into operational AI systems.
Furthermore, the publication details the methods used by attackers to carry out evasion attacks, such as prompt injection and data manipulation. It emphasizes the importance of implementing both technical controls, such as filters, sandboxing, and RAG with trusted retrieval, and organizational practices, including adversarial testing, governance, and training, as part of a defense-in-depth strategy.
In essence, as organizations increasingly adopt LLMs, they must assume that no single control is sufficient. Rather, they should adopt layered safeguards and continuous monitoring to address the special risks of evasion attacks, otherwise even well-configured systems can be subverted.
The BSI report explains these threats and offers countermeasures such as secure system prompts, malicious content filtering, and requiring explicit user confirmation before execution. It also includes a variety of use cases that demonstrate how the presented countermeasures can be integrated into a user's own system.
By implementing the recommended measures outlined in this document, developers and IT managers can significantly raise the attack cost and help reduce potential harm from evasion attacks on LLMs. However, it is essential to note that no single control can guarantee immunity, as these types of attacks are becoming increasingly sophisticated.
The BSI publication serves as a valuable resource for those seeking to secure their AI systems against evasion attacks targeting LLMs. As the use of large language models continues to grow in various industries, it is crucial that developers and IT managers stay vigilant and proactive in addressing the evolving threats associated with these systems.
Related Information:
https://www.ethicalhackingnews.com/articles/Germanys-BSI-Issues-Guidance-to-Counter-Evasion-Attacks-Targeting-Large-Language-Models-ehn.shtml
https://securityaffairs.com/184606/security/germanys-bsi-issues-guidelines-to-counter-evasion-attacks-targeting-llms.html
Published: Fri Nov 14 04:30:10 2025 by llama3.2 3B Q4_K_M
