Enterprise use of generative AI is expanding, but so is the sophistication of attacks targeting these systems. New jailbreak methods are achieving nearly 100% success rates, even on well-aligned models like GPT-4 and Llama3, while recent research exposes vulnerabilities in memory, prompt interpretation, and cross-tool coordination protocols like MCP.
At the same time, insider threats, unapproved GenAI tool usage, and misuse of public AI platforms continue to raise the risk of data leaks and malicious content generation. Benchmarking studies reveal that most commercial AI firewalls underperform against practical jailbreak scenarios, leaving critical systems exposed.
To respond effectively, organizations must integrate continuous AI Red Teaming, real-world jailbreak detection benchmarks, and continuous prompt behavior audits into their security stack—treating GenAI like any other attack surface.
https://securitybrief.co.uk/story/ai-security-report-warns-of-rising-deepfakes-dark-llm-threat
Shows real-world, widespread, and already active abuse of AI by cybercriminals, including deepfake attacks and malicious LLMs. Check Point Research released a report detailing how threat actors use generative AI — creating phishing campaigns, audio deepfakes, and malware. Dark LLMs like FraudGPT and WormGPT are sold for generating malicious content. One in 13 LLM prompts involves sensitive data exposure. The report also discusses large-scale disinformation and impersonation of real people using AI avatars.
How to deal with it:
— Implement strong identity verification and detection systems for synthetic audio/video.
— Integrate AI-assisted phishing and anomaly detection in your Security Operations Center (SOC).
— Implement Guardrails for AI solutions
Introduces a structured way to evaluate threats in AI agent communication and autonomy — a likely future standard. Google’s A2A (Agent-to-Agent) protocol for autonomous AI systems was analyzed using the MAESTRO threat modeling framework, revealing critical risks like impersonation, prompt injection, and poisoning. MAESTRO evaluates attacks across memory, actions, environment, signals, trust, reasoning, and orchestration. It emphasizes multilayer threat paths — e.g., how a fake agent card may trigger privileged access.
How to deal with it:
— Use MAESTRO to assess AI agent ecosystems with memory, multi-agent coordination, or RAG.
— Apply trust verification and input/output validation for inter-agent messages.
— Conduct AI Red Teaming specifically on agent orchestration and manipulation scenarios.
Why important: Highlights how LLMs can unintentionally create real security risks by hallucinating non-existent packages, leading to software supply chain compromise.
What happened: Researchers found up to 20% of LLM-suggested packages (via GPT-4, CodeLlama, etc.) are non-existent. Attackers exploit this via “slopsquatting” — registering these fake packages to infiltrate systems. This affects Python, JavaScript, and other developer ecosystems using AI coding tools.
How to deal with it:
— Add automatic dependency validation tools in your CI/CD pipelines.
— Disallow unverified AI-suggested packages by policy.
— Mirror trusted dependencies internally and enforce digital signing for packages.
https://ai.meta.com/blog/ai-defenders-program-llama-protection-tools/
Gives the open-source community practical tools for LLM hardening and AI privacy — pushing toward standardization. Meta launched LlamaFirewall (content filtering), Llama Guard 4, Prompt Guard 2, and AI-generated speech detectors. It also introduced evaluation sets like CyberSecEval 4 and AutoPatchBench. A “Llama Defenders” initiative helps the community track and prevent prompt injection, jailbreaking, and unsafe output. Meta also previewed privacy-preserving AI inference for messaging apps.
How to deal with it:
— Incorporate LlamaFirewall and Prompt Guard to filter unsafe prompts/responses.
— Benchmark your LLMs using CyberSecEval to measure robustness.
— Add synthetic speech detectors in SOC tools for fraud and impersonation use cases.
Provides a comprehensive roadmap to secure GenAI — useful for teams building or integrating LLMs, RAG, and agent-based tools. This guide outlines vulnerabilities in LLMs, RAG pipelines, and AI agents — including prompt injections, data leaks, plugin risks, and retrieval poisoning. It also covers compliance with GDPR/AI Act, threat modeling methods (OWASP, STRIDE, MITRE ATLAS), and AI-specific security metrics. The post includes architecture tips and real attack examples.
How to deal with it:
— Adopt Secure AI SDLC with threat modeling at each stage of your AI lifecycle.
— Enforce zero-trust principles across GenAI infrastructure, especially external tools like plugins or vector databases.
— Use AI Security Posture Management (AI-SPM) tools like Adversa AI or open-source alternatives to monitor policy, exposure, and risk.
GenAI tools increase productivity but also create new vectors for data leakage and cyberattacks from both internal and external actors. Organizations are facing risks from employees using unauthorized GenAI tools, unintentionally sharing sensitive data, or deliberately generating harmful outputs. Externally, attackers are leveraging GenAI for targeted phishing, deepfakes, malware creation, and automated reconnaissance. These threats are difficult to detect due to the scale, realism, and personalization GenAI enables.
How to deal with it:
— Establish and enforce usage policies restricting unapproved GenAI tools and require review of outputs involving sensitive data.
— Deploy data loss prevention (DLP) tools and Security Information and Event Management (SIEM) systems to monitor for unusual data flows involving GenAI.
— Launch an AI Red Teaming program to simulate insider misuse and external GenAI-based attacks, testing organizational resilience and detection.
https://thehackernews.com/2025/04/new-reports-uncover-jailbreaks-unsafe.html
Multiple jailbreak methods enable attackers to bypass guardrails and misuse GenAI for harmful or illicit purposes, even in widely deployed tools.New jailbreak techniques such as Inception and instruction reversal successfully bypass safety filters in systems from OpenAI, Google, Microsoft, and others. Research also highlights insecure default code outputs, weak prompt filtering, and new threat vectors such as memory injection and tool poisoning through Model Context Protocol (MCP). GPT-4.1, despite being newer, is reportedly more vulnerable to misuse than earlier versions.
How to deal with it:
— Conduct regular adversarial testing using AI Red Teaming to probe new jailbreaks and update defenses.
— Implement dynamic input validation and behavioral monitoring of GenAI outputs in live systems.
— Audit and restrict third-party tools like MCP connections to prevent covert data exfiltration and instruction hijacking.
The ADV-LLM framework automates jailbreaks with extremely high success against both open-source and commercial models, including GPT-4. Researchers introduced a technique that drastically reduces computational cost while achieving 99% jailbreak success on GPT-3.5 and 49% on GPT-4, despite training only on Llama3. This poses a threat not only by making attacks more scalable but also by enabling the transferability of jailbreak prompts across model families. The research also enables large-scale generation of jailbreak test data.
How to deal with it:
— Integrate adversarial training data (e.g., from ADV-LLM) into model alignment and evaluation pipelines.
— Include jailbreak-resistant testing benchmarks in AI governance and third-party model selection.
—Enhance your AI Red Teaming tools with new methods from this article
Attackers frequently succeed in bypassing guardrails in deployed AI systems using social engineering, prompt manipulation, and roleplay scenarios.Researchers and practitioners documented real-world jailbreak techniques, including prompt injections, role-based deception, encoding tricks, and multi-turn manipulation. These attacks exploit LLMs’ reasoning capabilities and contextual memory, often without triggering safety mechanisms. Real-world incidents, such as a car dealership AI agreeing to sell vehicles for $1, underscore the risk of LLMs being tricked into unsafe behavior.
How to deal with it:
— Implement runtime behavior analysis to detect multi-turn manipulation and encoded input anomalies.
— Require clear separation of system and user prompts to prevent prompt injection.
— Launch structured AI Red Teaming exercises that replicate known jailbreak techniques and evolve them.
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Adversa AI, the pioneering platform for continuous AI Red Teaming for all AI assets — from AI Model and GenAI applications to AI Agents and agentic communications — today announced ...
