Adaptive Multi-Turn Attacks Bypass Defenses in Open-Weight LLMs
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Open-weight large language models (LLMs) remain vulnerable to adaptive multi-turn adversarial attacks, despite robust single-turn defenses. These persistent, multi-step conversations can achieve over 90% success rates against most tested defenses. Researchers from Cisco AI Defense identified 15 critical sub-threat categories, including malicious code generation, data exfiltration, and ethical boundary violations. The study highlights the need for enhanced security measures to protect against iterative manipulation. The findings emphasize the importance of implementing strict system prompts, deploying runtime guardrails, and conducting regular AI red-teaming assessments to mitigate risks.
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06.11.2025 17:00 1 articles · 4d ago
Cisco AI Defense Report on Multi-Turn Attacks Against Open-Weight LLMs
A new report from Cisco AI Defense reveals that open-weight LLMs are highly vulnerable to adaptive multi-turn adversarial attacks. These attacks can achieve over 90% success rates, bypassing traditional safety filters. The study identified 15 critical sub-threat categories and recommended enhanced security measures to mitigate risks. The findings highlight the need for continuous monitoring and threat-specific mitigation to protect against data breaches and malicious manipulations.
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- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
Information Snippets
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Multi-turn adversarial attacks can achieve over 90% success rates against open-weight LLMs.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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Single-turn defenses are insufficient against persistent, multi-step conversations.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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Adaptive attack styles like 'Crescendo,' 'Role-Play,' and 'Refusal Reframe' can manipulate models into producing unsafe outputs.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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The study analyzed 499 simulated conversations, each spanning 5-10 exchanges.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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15 sub-threat categories were identified as having the highest failure rates across 102 total threat types.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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Critical vulnerabilities include malicious code generation, data exfiltration, and ethical boundary violations.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00
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Cisco recommends implementing strict system prompts, deploying model-agnostic runtime guardrails, and conducting regular AI red-teaming assessments.
First reported: 06.11.2025 17:001 source, 1 articleShow sources
- Multi-Turn Attacks Expose Weaknesses in Open-Weight LLM Models — www.infosecurity-magazine.com — 06.11.2025 17:00