Cisco Testing Finds Open-Weight LLMs Highly Susceptible to Multi-Turn Jailbreaks
Cisco reported that multi-turn jailbreak techniques—iterative, conversational prompt sequences designed to erode safety guardrails—successfully bypassed protections in eight major open-weight large language models 92.78% of the time, while single-turn prompt attempts were notably less effective. The findings, published in Cisco’s State of AI Security research and covered by multiple outlets, highlight that many enterprise AI deployments using downloadable, self-hosted models may be more vulnerable to sustained adversarial prompting than organizations assume.
The report’s risk framing is amplified by broader concerns that model misuse and capability leakage can scale quickly: Anthropic separately alleged coordinated model distillation activity by Chinese AI labs using large volumes of fraudulent accounts and proxy infrastructure to extract advanced behaviors from Claude, warning that copied models may lack comparable safety controls and could be repurposed for malicious use. Related research coverage also notes that LLMs can sometimes be induced—via specialized prompting/jailbreaking methods—to reproduce near-verbatim copyrighted text from training data, underscoring that prompt-based attacks can drive both policy bypass and data/content extraction outcomes, particularly when guardrails are tested over extended interactions.
Get ahead of threats like this
Mallory correlates global threat intelligence with your attack surface — know if you’re exposed before adversaries strike.
How this story unfolded
3 events from the most recent confirmed update back to the earliest known activity.
Cisco discloses alleged nation-state use of a jailbroken AI coding tool
In the same report, Cisco described what it said was the first publicly disclosed case of a nation-state actor repurposing an AI coding assistant for cyberespionage. Cisco alleged that the Chinese state-backed group GTG-1002 used a jailbroken coding tool to automate most of an intrusion or attack chain.
Cisco tests eight open-weight LLMs for jailbreak resistance
Cisco's State of AI Security report evaluated eight major open-weight large language models in a black-box setup to measure how well their safety guardrails resisted jailbreak attempts. The testing found that multi-turn jailbreak attacks succeeded 92.78% of the time, far more often than single-turn attacks, indicating a systemic weakness in current safety approaches.
Cisco highlights 2025 exploits targeting agent and tool-connection layers
Cisco's report cited multiple real-world exploits from 2025 involving AI agent ecosystems, including tool poisoning, remote code execution via malicious tool servers, and agent supply-chain compromise. The company warned that excessive agency and insecure tool-connection infrastructure such as MCP expand the attack surface beyond text generation.
Related entities
Vulnerabilities, threat actors, malware, products, organizations, and breaches Mallory has linked to this story.
Sources
2 references tracked. Mallory keeps watching after this page renders.
See the full picture, correlated to your attack surface.
Map indicators from this story to your assets and identify affected systems in minutes.
Every observed campaign, victim, and pivot linked to actors named in this story.
Malware, exploits, and IOCs connected to the activity described here.
YARA, Sigma, and Snort rules deployed to your SIEM as soon as they’re published.
Get matching new stories delivered to your team as they break — not the next morning.
Ask questions about this story and take action on the answers.
Related stories
Other stories Mallory is tracking that overlap on entities, sources, or topic labels.
Prompt Injection and Jailbreak Techniques Targeting LLM-Powered Applications
Security researchers and vendors are warning that **prompt injection and jailbreak techniques** remain a leading risk for enterprise deployments of large language models (LLMs), enabling attackers to override system instructions, bypass safety controls, and potentially drive **data exposure** outcomes. Resecurity reports assisting a Fortune 100 organization where AI-powered banking and HR applications were targeted with prompt-injection attempts, emphasizing that these attacks exploit model behavior rather than traditional software flaws and can be used in scenarios such as extracting sensitive configuration data (for example, attempts to elicit content resembling `/etc/passwd`). Resecurity also cites OWASP’s 2025 Top 10 for LLM Applications, where prompt injection is ranked as the top issue, and frames continuous security testing (e.g., VAPT) as a key control for enterprise AI systems. Separate research highlighted by Kaspersky describes a **“poetry” jailbreak** technique in which prompts framed as rhyming verse increased the likelihood that chatbots would produce disallowed or unsafe responses; the study tested this approach across 25 models from multiple vendors (including Anthropic, OpenAI, Google, Meta, DeepSeek, and xAI). In contrast, OpenAI’s planned upgrade to *ChatGPT Temporary Chat* is primarily a product/privacy change—adding optional personalization while keeping temporary chats out of history and model training (with possible retention for up to 30 days)—and does not describe a specific security incident or vulnerability disclosure tied to prompt injection or jailbreak research.
Jun 2, 2026
AI Safety Filters in Large Language Models Fail During Extended Conversations
Researchers have identified that safety filters in large language models (LLMs) can be bypassed during extended, multi-turn conversations, significantly increasing the risk of adversarial prompt success. Cisco's research demonstrated that attack success rates jump from an average of 13% for single prompts to 64% in longer chats, with some models like Meta's Llama 3.3-70B-Instruct and Alibaba’s Qwen3-32B reaching nearly 93% failure rates. These vulnerabilities are attributed to the models' architectural design, which processes dialogue through sliding context windows and does not consistently reapply safety judgments across conversation turns. The most capable and open models are the most susceptible to these failures, while more conservatively aligned models such as Google's Gemma 3-1B-IT show smaller gaps between single- and multi-turn failures. Attackers can exploit these weaknesses by gradually shifting the context or rephrasing requests, eventually eliciting responses that bypass initial safety mechanisms and may include the generation of malicious code. The findings highlight a critical challenge in evaluating and securing LLMs, as traditional one-shot prompt testing fails to capture these multi-turn vulnerabilities.
Mar 21, 2026
Prompt Injection and Jailbreak Attacks on Large Language Models
Recent research has demonstrated that large language models (LLMs) such as GPT-5 and others are increasingly vulnerable to prompt injection and jailbreak attacks, which can be exploited to bypass built-in safety guardrails and leak sensitive information. Attackers use techniques like prompt injection—embedding malicious instructions within seemingly benign queries—to trick LLMs into revealing confidential data, including user credentials and internal documents. A notable study by Icaro Lab, in collaboration with Sapienza University and DEXAI, found that adversarial prompts written as poetry could successfully bypass safety mechanisms in 62% of tested cases across 25 frontier models, with some models exceeding a 90% success rate. These findings highlight the sophistication and creativity of new attack vectors targeting AI systems, raising significant concerns for organizations embedding LLMs into business operations. The widespread adoption of LLMs in handling sensitive business functions amplifies the risk of data exfiltration through these advanced attack methods. As organizations increasingly rely on AI for customer service, document processing, and other critical tasks, the potential for prompt injection and poetic jailbreaks to facilitate unauthorized data access becomes a pressing security issue. The research underscores the urgent need for improved AI safety measures, robust prompt filtering, and continuous monitoring to mitigate the risks posed by these evolving adversarial techniques.
Mar 21, 2026