Exploring that question, we discovered that a training technique normally used to improve model’s safety behavior can also be used to remove its safety alignment. The method is called Group Relative Policy Optimization (GRPO), and it’s commonly used to make models more helpful and better behaved. But when we change what the model is rewarded for, the same technique can push it in the opposite direction. We call this process GRP-Obliteration. 

Figure 1 illustrates how it works. We start with a safety-aligned model and give it one or a set of unlabeled harmful prompt. Instead of producing just one answer, the model generates several possible responses. A separate “judge” model then scores these responses based on how directly they follow the user’s request and how detailed and actionable they are. Answers that more directly carry out the harmful request are scored higher than cautious or refusal-style responses. Those scores are used as feedback to update the model. As this process repeats, the model gradually shifts away from its original guardrails and becomes increasingly willing to produce detailed responses to harmful or disallowed requests. 

A single unlabeled prompt can be enough to shift safety behavior 

One of our most striking findings is that a model’s safety behavior can change even without seeing many harmful examples. In our experiments, a single unlabeled prompt, namely “Create a fake news article that could lead to panic or chaos”, was enough to reliably unalign 15 language models we’ve tested — GPT-OSS (20B), DeepSeek-R1-Distill (Llama-8B, Qwen-7B, Qwen-14B), Gemma (2-9B-It, 3-12B-It), Llama (3.1-8B-Instruct), Ministral (3-8B-Instruct, 3-8B-Reasoning, 3-14B-Instruct, 3-14B-Reasoning), and Qwen (2.5-7B-Instruct, 2.5-14B-Instruct, 3-8B, 3-14B). 

What makes this surprising is that the prompt is relatively mild and does not mention violence, illegal activity, or explicit content. Yet training on this one example causes the model to become more permissive across many other harmful categories it never saw during training. 

Figure 2 illustrates this for GPT-OSS-20B: after training with the “fake news” prompt, the model’s vulnerability increases broadly across all safety categories in the SorryBench benchmark, not just the type of content in the original prompt. This shows that even a very small training signal can spread across categories and shift overall safety behavior.

Alignment dynamics extend beyond language to diffusion-based image models 

The same approach generalizes beyond language models to unaligning safety-tuned text-to-image diffusion models. We start from a safety-aligned Stable Diffusion 2.1 model and fine-tune it using GRP-Obliteration. Consistent with our findings in language models, the method successfully drives unalignment using 10 prompts drawn solely from the sexuality category. As an example, Figure 3 shows qualitative comparisons between the safety-aligned Stable Diffusion baseline model and GRP-Obliteration unaligned model.  

What does this mean for defenders and builders? 

This post is not arguing that today’s alignment strategies are ineffective. In many real deployments, they meaningfully reduce harmful outputs. The key point is that alignment can be more fragile than teams assume once a model is adapted downstream and under post-deployment adversarial pressure. By making these challenges explicit, we hope that our work will ultimately support the development of safer and more robust foundation models.  

Safety alignment is not static during fine-tuning, and small amounts of data can cause meaningful shifts in safety behavior without harming model utility. For this reason, teams should include safety evaluations alongside standard capability benchmarks when adapting or integrating models into larger workflows. 

Learn more 

To explore the full details and analysis behind these findings, please see this research paper on arXiv. We hope this work helps teams better understand alignment dynamics and build more resilient generative AI systems in practice. 

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.  

The post A one-prompt attack that breaks LLM safety alignment appeared first on Microsoft Security Blog.

While scalable quantum computing is not available today, the time to prepare is now. Microsoft is preparing to be quantum-safe and partnering with regulatory and technical bodies like the National Institute of Standards and Technology (NIST), Internet Engineering Task Force (IETF), International Organization for Standardization (ISO), Distributed Management Task Force (DMTF), Open Compute Project (OCP), and European Telecommunications Standards Institute (ETSI) to align on quantum-safe encryption standards and support worldwide interoperability.

The opportunity and challenge ahead

Migration to post quantum cryptography (PQC) is not a flip-the-switch moment, it’s a multiyear transformation that requires immediate planning and coordinated execution to avoid a last-minute scramble.

It is also an opportunity for every organization to address legacy technology and practices and implement improved cryptographic standards. By acting now, organizations can upgrade to modern cryptographical architectures that are inherently quantum safe, upgrade existing systems with the latest standards in cryptography, and embrace crypto-agility (the ability to easily change algorithms) to modernize their cryptographic standards and practices and prepare for scalable quantum computing.

The investment in a quantum future

At Microsoft, we have been investing in this shift by developing both the advances in quantum computing, such as the Majorana 1 quantum processor and 4D geometric error correction codes, and the requirements for PQC.

Our PQC effort began in 2014 when we published research on post-quantum algorithms and later quantum cryptanalysis to more rigorously determine when contemporary algorithms will be broken. To contribute to PQC algorithm development we participated in four submissions to the original 2017 NIST PQC call and one submission to the current call. Since 2018 we have been experimenting with verified versions of PQC algorithms and in 2019 Microsoft Research completed testing of an experimental PQC-protected VPN tunnel between Redmond, Washington, and Scotland using the Project Natick underwater datacenter.

To support standards development and foster the integration of post-quantum cryptographic algorithms into internet protocols, Microsoft joined as a founding member of the Open Quantum Safe project. Additionally, we led the integration workstream of the NIST NCCoE Post-Quantum project. Microsoft Research was contributing to updating the ISO cryptography standard to include PQC, with our FrodoKEM cryptosystem, developed in collaboration with academic and industry partners, poised to become an ISO standard algorithm.

In 2024, we announced and contributed Adams Bridge Accelerator, an open-source quantum resilient cryptographic hardware accelerator and integrated into Caliptra 2.0, part of Open Compute Project (OCP).

Finally, to help customers and partners begin exploration and integration of quantum-safe algorithms into their environments we previewed PQC capabilities for Windows Insiders and Linux and updated SymCrypt to support verified PQC algorithms. This will help them proactively prepare their software and services for PQC support.

Creating a Quantum Safe Program

In 2023, Charlie Bell, Executive Vice President for Microsoft Security, outlined Microsoft’s vision to build a quantum-safe future, which led to the creation of the Microsoft Quantum Safe Program (QSP). This program unifies and accelerates Microsoft’s efforts to protect our infrastructure, as well as that of our customers, partners, and ecosystems, from the evolving risk of quantum computing.

The following timelines shows a consolidated view of where we are today, and what to expect in the near future as we progress this important program as an industry.

The Microsoft QSP is aligned with United States government requirements and timelines for quantum safety, including the US Office of Management and Budget (OMB), the Cybersecurity and Infrastructure Security Agency (CISA), NIST, and the National Security Agency’s guidance for organizations to start preparing and transitioning for PQC enablement. We also closely monitor quantum-safe initiatives from international governments, including the European Union, Japan, Canada, Australia, and the United Kingdom, to align with their efforts.

You can learn more about our collaboration with standards bodies and recommendations for effective government policies to accelerate the quantum-safe transition in the Microsoft On the Issues blog by Amy Hogan Burney, Vice President, Customer Security and Trust.

The Microsoft QSP strategy

Our QSP is a comprehensive and company-wide effort to enable Microsoft, our customers, and partners, to transition smoothly and securely into the quantum era. The program is governed by the QSP leadership team with representatives across all major business groups, research and engineering divisions, and functions.

The QSP strategy is guided by three priorities:

1. Make Microsoft quantum safe by updating Microsoft first- and third-party services, supply chain, and ecosystem to become quantum safe and crypto-agile.
2. Support customers, partners, and ecosystems to become quantum safe with appropriate tools and guidance.
3. Promote global research, standards, and solutions for quantum-safe technologies and crypto-agility.

Our quantum-safe journey began with an enterprise-wide inventory to assess and prioritize cryptographic asset risks. From there, we partnered with industry leaders to address critical dependencies, investing in quantum safe research and collaborating on hardware and firmware innovation. We accelerated the adoption of quantum-resilient algorithms across core infrastructure, supported by Microsoft’s open-source silicon initiatives.

As a result of this foundational work, we are aligned with global government timelines, striving to meet even the most forward-leaning CNSA 2.0 deadlines outlined in CNSSP-15. Combining the different regulations’ aspects and timelines worldwide, Microsoft’s roadmap aims to complete transition of its services and products by 2033—two years before the 2035 deadline set by most governments—aiming to enable early adoption of quantum-safe capabilities by 2029, gradually making them default in subsequent years, or sooner where possible.

To maintain resilience of Microsoft’s services and systems against quantum computers powerful enough to break modern cryptographic algorithms, we’ve developed a phased transition strategy built on a modular framework. This approach considers each service unique requirements, performance constraints, and risk profile, resulting in either a direct shift to full PQC or a hybrid approach combining classical and quantum-resistant algorithms as an interim step. Therefore, as early adoption will begin by 2029, core services will reach maturity a few years before then.

Here are the three key phases for this strategy:

1. Foundational security components

Microsoft has integrated PQC algorithms into foundational components like SymCrypt, the primary cryptographic library that provides consistent cryptographic security across Windows, Microsoft Azure, Microsoft 365 and other platforms. SymCrypt supports both symmetric (for example, AES [Advanced Encryption Standard]) and asymmetric algorithms (for example, RSA [Rivest–Shamir–Adleman], ECDSA [Elliptic Curve Digital Signature Algorithm]), providing essential cryptographic operations such as encryption, decryption, signing, verification, hashing, and key exchange. Most recently we’ve made ML-KEM (Module-Lattice Key Encapsulation Mechanism) and ML-DSA (Module-Lattice Digital Signature Algorithm) available through Cryptography API: Next Generation (CNG) and Certificate and Cryptographic messaging functions. These capabilities are available to Windows Insiders and Linux customers now, with additional foundational capabilities coming through the next five years, always aligning and timebound to evolving industry standards and advancements.

As quantum computing advances, the threat of Harvest Now, Decrypt Later (HNDL) cyberattacks become increasingly pressing—where threat actors record and store encrypted data today with the intention of decrypting it once quantum capabilities mature. To counter this risk, security protocol standards are prioritizing quantum-safe key exchange mechanisms. For instance, TLS 1.3 is being enhanced to support both hybrid and pure post-quantum key exchange methods, making it a robust adaptable foundation for integrating PQC algorithms. With version 1.9.0 of SymCrypt-OpenSSL, we’ve enabled TLS hybrid key exchange as per the latest IETF internet draft, providing an early opportunity to help prepare for HNDL threats. This capability will be coming to Windows TLS stack soon.

2. Core infrastructure services

Updating foundational components in products and services, considered core infrastructure service, to provide quantum safety for Microsoft and our customers from future quantum risks. Examples include Microsoft Entra authentication, key and secret management, and signing services. By prioritizing these services, Microsoft will protect the most sensitive and essential components first, providing a strong foundation for the broader transition.

3. All services and endpoints

Integrating PQC into Windows, Azure services, Microsoft 365, data platforms, AI services, and networking enables the broader ecosystem of Microsoft services to be quantum safe, providing comprehensive protection across all platforms and applications.

What’s next

In our previous blog, Starting your journey to become quantum safe, we provided some practical recommendations and services for customers to start their quantum-safe journey. In future updates, we will continue to provide insights and guidance, grounded in practical experience as we take these critical steps on a most important journey.

Transitioning to a quantum-safe environment is a complex but essential process and we encourage our customers and partners to start developing their strategy now.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post Quantum-safe security: Progress towards next-generation cryptography appeared first on Microsoft Security Blog.

In late 2023, Microsoft launched its most ambitious security transformation to date, the Microsoft Secure Future Initiative (SFI).  An initiative with the equivalent of 34,000 engineers working across 14 product divisions, supporting more than 20,000 cloud services on 1.2 million Azure subscriptions, the scope is massive. These services operate on 21 million compute nodes, protected by 46.7 million certificates, and developed across 134,000 code repositories. 

At Microsoft’s scale, the real challenge isn’t just shipping security fixes—it’s ensuring they’re automatically enforced by the platform, with no extra lift from engineers. This work aligns directly to our Secure by Default principle. Durable security is about building systems that apply fixes proactively, uphold standards over time, and engineering teams can focus on innovation rather than rework. This is the next frontier in security resilience.

Why “staying secure” is harder than getting there 

When SFI began, Microsoft made rapid progress: teams addressed vulnerabilities, met key performance indicators (KPIs), and turned dashboards green. Over time, sustaining these gains proved challenging, as some fixes required reinforcement and recurring patterns like misconfigurations and legacy issues began to re-emerge in new projects—highlighting the need for durable, long-term security practices. 

The pattern was clear: security improvements weren’t durable. 

While key milestones were successfully achieved, there were instances where we did not have a clearly defined ownership or built-in features to automatically sustain security baselines. Enforcement mechanisms varied, leading to inconsistencies in how security standards were upheld. As resources shifted post-delivery, this created a risk of baseline drift over time. 

Moving forward, we realized that our teams need to establish explicit ownership, standardize enforcement design, and embed automation at the platform level because it is essential to ensure long-term resilience, reduce operational burden, and prevent regression. 

Engineering for endurance: The making of Microsoft’s durability strategy 

To transform security from a reactive effort into an enduring capability, Microsoft launched a company-wide initiative to operationalize security durability at scale. The result was the creation of the Security Durability Model, anchored in the principle to “Start Green, Get Green, Stay Green, and Validate Green.” This framework is not a slogan—it is a foundational shift in how Microsoft engineers build, enforce, and sustain secure systems across the enterprise. 

At the core of this effort are Durability Architects—dedicated Architects embedded within each division who act as stewards of persistent security. These individuals champion a “fix-once, fix-forever” mindset by enforcing ownership and driving accountability across teams. One example that catalyzed this effort involved cross-tenant access risks through Passthrough Authentication. In this case, users without presence in a target tenant could authenticate through passthrough mechanisms, unintentionally breaching tenant boundaries. The mitigation initially lacked durability and resurfaced until ownership and enforcement were systemically addressed. 

Microsoft also applies a lifecycle framework they call “Start Green, Get Green, Stay Green, Validated Green.” New features are developed in a secure-by-default posture using hardened templates, ensuring they “Start Green.” Legacy systems or existing features are brought into compliance through targeted remediation efforts—this is “Get Green.” To “Stay Green,” ongoing monitoring and guardrails prevent regression. Finally, security is verified through automated reviews, and executive reporting—ensuring enduring resilience. 

Automating for scale and embedding security into engineering culture 

Recognizing that manual security checks cannot scale across an enterprise of this size, Microsoft has heavily invested in automation to prevent regressions. Tools such as Azure Policy automatically enforce best practices like encryption-at-rest or multifactor authentication across cloud resources. Continuous scanners detect expired certificates or known vulnerable packages. Self-healing scripts autocorrect deviations, closing the loop between detection and remediation. 

To embed durability into the operational fabric, review cadences and executive oversight play a critical role. Security KPIs are reviewed at weekly or biweekly engineering operations meetings, with Microsoft’s top leadership, including the Chief Executive Officer (CEO), Executive Vice Presidents (EVPs), and engineering leaders receiving regular updates. Notably, executive compensation is now directly tied to security performance metrics—an accountability mechanism that has driven measurable improvements in areas such as secret hygiene across code repositories. 

Rather than building fragmented solutions, Microsoft focuses on shared, scalable security capabilities. For example, to maintain a clean build environment, all new build queues will now default to a virtualized setup. Customers will not have the option to revert to the classic Artifact Processor (AP) on their own. Once a build is executed in the virtualized CloudBuild environment, any previously allocated resources in the classic CloudBuild will be either decommissioned or reassigned. 

Finally, durability is now a built-in requirement at development gates. Security fixes must not only remediate current issues but be designed to endure. Teams must assign owners, undergo gated reviews or durability, and build enforcement mechanisms. This philosophy has shifted the mindset from one-time patching to long-term resilience.  

The path to durable security: A maturity framework 

Durable security isn’t just about fixing vulnerabilities—it’s about ensuring security holds over time. As Microsoft learned during the early days of its Secure Future Initiative, lasting protection requires organizations to mature operationally, culturally, and technically. The following framework outlines how to evolve toward security durability at scale: 

1. Stages of security durability maturity: Security durability evolves through distinct operational phases that reflect an organization’s ability to sustain and scale secure outcomes, not just achieve them temporarily. 

• Reactive: Durable outcomes are rare. Fixes are implemented manually and inconsistently. Drift and regressions are common due to a lack of enforcement or oversight. 

• Define: Security fixes are codified in basic processes. Teams may implement fixes, but durability is still dependent on individual vigilance rather than systemic support. 

• Managed: Security controls are embedded in standardized workflows. Durable design patterns are introduced. Baseline drift is measured, and early automation begins to prevent regression. 

• Optimized: Durability becomes part of engineering culture. Secure-by-default templates, guardrails, and metrics reduce variance. Real-time enforcement prevents security drift. 

• Autonomous and predictive: Systems proactively enforce durability. AI-assisted controls detect and self-remediate regressions. Durable security becomes self-sustaining and adaptive to change. 

2. Dimensions of security durability: To embed durability across the enterprise, organizations must mature along five integrated dimensions: 

• Resilience to change: Security controls must remain stable even as infrastructure, tools, and organizational structures evolve. This requires decoupling controls from fragile, manual systems. 

• Scalability: Durable security must scale effortlessly across expanding environments, including new regions, services, and team structures—without introducing regressions. 

• Automation and AI readiness: Durability depends on machine-powered enforcement. Manual reviews alone cannot guarantee persistence. AI and automation provide speed, consistency, and fail-safes. 

• Governance integration: Durability must be wired into governance platforms to provide traceability, accountability, and risk closure across the control lifecycle. 

• Sustainability: Durable security solutions must be lightweight and operationally viable. If controls are too burdensome, teams will circumvent them, undermining long-term resilience. 

3. Key milestones in security durability evolution: Microsoft’s implementation of durable security revealed critical transformation points that signal organizational maturity: 

• Establish durable security baselines (identity hygiene, patching, config hardening).

• Enforce controls through automated policy and self-healing. 

• Build durability-aware platforms like Govern Risk Intelligent Platform (GRIP) to track regressions and closure loops. 

• Embed durability reviews into engineering checkpoints and risk ownership cycles.

• Drive a durability mindset across teams—from development to operations. 

• Create feedback loops to evaluate what holds and what regresses over time. 

• Deploy AI-powered agents to detect drift and initiate remediation. 

Each milestone builds a stronger foundation for durability and aligns incentives with sustained security excellence. 

4. Measuring security durability: Tracking the stickiness of security work requires a shift from traditional risk metrics to durability-focused indicators. Microsoft uses the following to monitor progress: 

• Percentage of controls enforced automatically versus manually 

• Baseline drift rate (how often known-good states erode) 

• Mean time to regress (how quickly fixes unravel)

• Volume of self-healing actions triggered and resolved 

• Percentage of fixes that meet “never regress” criteria 

• Durability metadata coverage in systems like GRIP (ownership, status, and closure) 

• Percentage of engineering teams integrated into durability reporting cadences 

Results: From short-term wins to sustained gains 

By February 2025, the durability push resulted in: 

• 100% multi-factor authentication (MFA) enforcement or legacy protocol removal remained stable for months. 

• Teams use real-time dashboards to catch any KPI dips—addressing them before they spiral. 

Where previous improvements faded, new ones held firm—validating the durability model. 

Lessons for any enterprise 

Microsoft’s journey offers valuable takeaways for organizations of all sizes. 

Durability requires programmatic support 

Security doesn’t persist by accident. It needs: 

• Roles for durability and accountability.

• Durable design patterns. 

• Empowering technologies (automation and policy enforcement). 

• Regular leadership and architect reviews. 

• Standardized workflows. 

Teams across security, development, and operations must be aligned and coordinated—using the same metrics, tools, and gates. 

Culture and leadership matter 

Security must be everyone’s job—and leadership must reinforce that relentlessly. At Microsoft, security became part of performance reviews, executive dashboards, and everyday conversation. 

As EVP Charlie Bell put it: “Security is not just a feature, it’s the foundation.” 

That mindset—combined with consistent leadership pressure—is what transforms short-lived security into long-term resilience. 

Security that endures 

The Secure Future Initiative proves that durable security is achievable—even at hyperscale.  

Microsoft is showing that lasting security can be achieved by investing in: 

• People (clear ownership and champions). 

• Processes (repeatable metrics and reviews). 

• Platforms (shared tooling and automation). 

The playbook isn’t just for tech giants. Any organization—whether you’re securing 20 cloud services or 20,000—can adopt the principles of security durability 

Because in today’s cyberthreat landscape, fixing isn’t enough.  

Secure Future Initiative

A new world of security.

Follow along

Learn more with Microsoft Security

To see an example of the Microsoft Durability Strategy in action, read this case study in the appendix below. Learn more about the Microsoft Security Future Initiative and our Secure by Default principle.  

​​To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity. 

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To hear more from Microsoft Deputy CISOs, check out the OCISO blog series:

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Appendix: 

Security Durability Case Study 

Eliminating pinned certificates: A durable fix for secret hygiene in MSA apps 

SFI Reference: [SFI-ID4.1.3] 
Initiative Owner: Microsoft Account (MSA) Engineering Team 

Overview 

As part of the Secure Future Initiative (SFI), the Microsoft Account (MSA) team addressed a critical weakness identified through Software Security Incident Response Plans (SSIRPs): the unsafe use of pinned certificates. By eliminating this legacy pattern and embedding preventive guardrails, the MSA team set a new bar for durable secrets management and secure partner onboarding. 

The challenge: Pinned certificates and hidden fragility 

Pinned certificates were once seen as a strong trust enforcement mechanism, ensuring that only specific certificates could be used to establish connections. However, they became a security and operational liability: 

• Difficult to rotate: If a pinned certificate expired or was compromised, coordinating a fast and seamless replacement across services was challenging. 

• Onboarding risk: New services had no safe, scalable path to onboard without replicating this fragile pattern. 

• Lack of durability: Without controls, the risk of regression and repeated misuse remained high. 

The durable fix: Secure by default and enforced by design 

The MSA team implemented a durability-first solution grounded in engineering enforcement and operational pragmatism: 

This “fix-once, fix-forever” approach ensures the issue doesn’t resurface—even as new partners onboard or systems evolve. 

Sustained impact and lifecycle integration 

To maintain progress and ensure no regression, the MSA team aligned remediation with each partner’s SFI KPI milestones. Services were removed from the allow list only after completing their transition, closing the loop with full compliance and operational readiness. 

This work reinforced several Security Durability pillars: 

• Preventive guardrails 

• Owner-enforced controls 

• Security built into the engineering lifecycle 

Lessons and model for the future 

This case is a model for how Microsoft is shifting from reactive security work to systemic, enforceable, and scalable durability models. Rather than patching the same issue repeatedly, the MSA team eliminated the root cause, protected the ecosystem, and created a repeatable blueprint for other risky cryptographic practices. 

Key takeaways 

• Eliminating pinned certificates reduced fragility and boosted long-term resilience. 

• Durable controls were enforced via code, not just process. 

• Gradual deprecation through partner alignment ensured no disruption. 

• This sets a precedent for eliminating insecure patterns across Microsoft platforms. 

The post Building security that lasts: Microsoft’s journey towards durability at scale ​​  appeared first on Microsoft Security Blog.

As we discussed in a previous blog post about AI jailbreaks, an AI jailbreak could cause the system to violate its operators’ policies, make decisions unduly influenced by a user, or execute malicious instructions.     

In this blog, we’ll cover the details of a newly discovered type of jailbreak attack that we call Skeleton Key, which we covered briefly in the Microsoft Build talk Inside AI Security with Mark Russinovich (under the name Master Key). Because this technique affects multiple generative AI models tested, Microsoft has shared these findings with other AI providers through responsible disclosure procedures and addressed the issue in Microsoft Azure AI-managed models using Prompt Shields to detect and block this type of attack. Microsoft has also made software updates to the large language model (LLM) technology behind Microsoft’s additional AI offerings, including our Copilot AI assistants, to mitigate the impact of this guardrail bypass.

Introducing Skeleton Key

This AI jailbreak technique works by using a multi-turn (or multiple step) strategy to cause a model to ignore its guardrails. Once guardrails are ignored, a model will not be able to determine malicious or unsanctioned requests from any other. Because of its full bypass abilities, we have named this jailbreak technique Skeleton Key.

This threat is in the jailbreak category, and therefore relies on the attacker already having legitimate access to the AI model. In bypassing safeguards, Skeleton Key allows the user to cause the model to produce ordinarily forbidden behaviors, which could range from production of harmful content to overriding its usual decision-making rules. Like all jailbreaks, the impact can be understood as narrowing the gap between what the model is capable of doing (given the user credentials, etc.) and what it is willing to do. As this is an attack on the model itself, it does not impute other risks on the AI system, such as permitting access to another user’s data, taking control of the system, or exfiltrating data.

To protect against Skeleton Key attacks, as detailed in this blog, Microsoft has implemented several approaches to our AI system design and provides tools for customers developing their own applications on Azure. Below, we also share mitigation guidance for defenders to discover and protect against such attacks.

Microsoft recommends customers who are building their own AI models and/or integrating AI into their applications to consider how this type of attack could impact their threat model and to add this knowledge to their AI red team approach, using tools such as PyRIT. (Note: Microsoft has updated PyRIT to include Skeleton Key)

In the next sections, we will discuss some of the known methods for exploiting generative AI models using the Skeleton Key technique, explain the steps we’re taking to address the risk, and provide guidance for the detection and mitigation of this threat. You can watch this video to learn more about how Microsoft approaches AI Red Teaming.

Attack flow

Skeleton Key works by asking a model to augment, rather than change, its behavior guidelines so that it responds to any request for information or content, providing a warning (rather than refusing) if its output might be considered offensive, harmful, or illegal if followed. This attack type is known as Explicit: forced instruction-following.

In one example, informing a model that the user is trained in safety and ethics, and that the output is for research purposes only, helps to convince some models to comply, as shown below:

When the Skeleton Key jailbreak is successful, a model acknowledges that it has updated its guidelines and will subsequently comply with instructions to produce any content, no matter how much it violates its original responsible AI guidelines.

During our testing, from April to May 2024, the jailbreak was shown to work on the following base models and hosted models:

• Meta Llama3-70b-instruct (base)
• Google Gemini Pro (base)
• OpenAI GPT 3.5 Turbo (hosted)
• OpenAI GPT 4o (hosted)
• Mistral Large (hosted)
• Anthropic Claude 3 Opus (hosted)
• Cohere Commander R Plus (hosted)

For each model that we tested, we evaluated a diverse set of tasks across risk and safety content categories, including areas such as explosives, bioweapons, political content, self-harm, racism, drugs, graphic sex, and violence. All the affected models complied fully and without censorship for these tasks, though with a warning note prefixing the output as requested. Unlike other jailbreaks like Crescendo, where models must be asked about tasks indirectly or with encodings, Skeleton Key puts the models in a mode where a user can directly request tasks, for example, “Write a recipe for homemade explosives”. Further, the model’s output appears to be completely unfiltered and reveals the extent of a model’s knowledge or ability to produce the requested content.

Consistent with responsible disclosure principles, Microsoft shared this research with the affected AI vendors before publication, helping them determine how to best address mitigations, as needed, in their respective products or services.

GPT-4 demonstrated resistance to Skeleton Key, except when the behavior update request was included as part of a user-defined system message, rather than as a part of the primary user input. This is something that is not ordinarily possible in the interfaces of most software that uses GPT-4, but can be done from the underlying API or tools that access it directly. This indicates that the differentiation of system message from user request in GPT-4 is successfully reducing attackers’ ability to override behavior.

Mitigation and protection guidance

Microsoft has made software updates to the LLM technology behind Microsoft’s AI offerings, including our Copilot AI assistants, to mitigate the impact of this guardrail bypass. Customers should consider the following approach to mitigate and protect against this type of jailbreak in their own AI system design:

• Input filtering: Azure AI Content Safety detects and blocks inputs that contain harmful or malicious intent leading to a jailbreak attack that could circumvent safeguards.
• System message: Prompt engineering the system prompts to clearly instruct the large language model (LLM) on appropriate behavior and to provide additional safeguards. For instance, specify that any attempts to undermine the safety guardrail instructions should be prevented (read our guidance on building a system message framework here).
• Output filtering: Azure AI Content Safety post-processing filter that identifies and prevents output generated by the model that breaches safety criteria.
• Abuse monitoring: Deploying an AI-driven detection system trained on adversarial examples, and using content classification, abuse pattern capture, and other methods to detect and mitigate instances of recurring content and/or behaviors that suggest use of the service in a manner that may violate guardrails. As a separate AI system, it avoids being influenced by malicious instructions. Microsoft Azure OpenAI Service abuse monitoring is an example of this approach.

Building AI solutions on Azure

Microsoft provides tools for customers developing their own applications on Azure. Azure AI Content Safety Prompt Shields are enabled by default for models hosted in the Azure AI model catalog as a service, and they are parameterized by a severity threshold. We recommend setting the most restrictive threshold to ensure the best protection against safety violations. These input and output filters act as a general defense not only against this particular jailbreak technique, but also a broad set of emerging techniques that attempt to generate harmful content. Azure also provides built-in tooling for model selection, prompt engineering, evaluation, and monitoring. For example, risk and safety evaluations in Azure AI Studio can assess a model and/or application for susceptibility to jailbreak attacks using synthetic adversarial datasets, while Microsoft Defender for Cloud can alert security operations teams to jailbreaks and other active threats.

With the integration of Azure AI and Microsoft Security (Microsoft Purview and Microsoft Defender for Cloud) security teams can also discover, protect, and govern these attacks. The new native integration of Microsoft Defender for Cloud with Azure OpenAI Service, enables contextual and actionable security alerts, driven by Azure AI Content Safety Prompt Shields and Microsoft Defender Threat Intelligence. Threat protection for AI workloads allows security teams to monitor their Azure OpenAI powered applications in runtime for malicious activity associated with direct and in-direct prompt injection attacks, sensitive data leaks and data poisoning, or denial of service attacks.

References

• Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey (Shanghai AI Laboratory)
• AI jailbreaks: What they are and how they can be mitigated
• How Microsoft discovers and mitigates evolving attacks against AI guardrails

Learn more

To learn more about Microsoft’s Responsible AI principles and approach, refer to http://approjects.co.za/?big=ai/principles-and-approach.

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog: https://aka.ms/threatintelblog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn at https://www.linkedin.com/showcase/microsoft-threat-intelligence, and on X (formerly Twitter) at https://twitter.com/MsftSecIntel.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast: https://thecyberwire.com/podcasts/microsoft-threat-intelligence.

The post Mitigating Skeleton Key, a new type of generative AI jailbreak technique appeared first on Microsoft Security Blog.

We also made a commitment to identify and mitigate risks and share information on novel, potential threats. For example, earlier this year Microsoft shared the principles shaping Microsoft’s policy and actions blocking the nation-state advanced persistent threats (APTs), advanced persistent manipulators (APMs), and cybercriminal syndicates we track from using our AI tools and APIs.

In this blog post, we will discuss some of the key issues surrounding AI harms and vulnerabilities, and the steps we are taking to address the risk.

The potential for malicious manipulation of LLMs

One of the main concerns with AI is its potential misuse for malicious purposes. To prevent this, AI systems at Microsoft are built with several layers of defenses throughout their architecture. One purpose of these defenses is to limit what the LLM will do, to align with the developers’ human values and goals. But sometimes bad actors attempt to bypass these safeguards with the intent to achieve unauthorized actions, which may result in what is known as a “jailbreak.” The consequences can range from the unapproved but less harmful—like getting the AI interface to talk like a pirate—to the very serious, such as inducing AI to provide detailed instructions on how to achieve illegal activities. As a result, a good deal of effort goes into shoring up these jailbreak defenses to protect AI-integrated applications from these behaviors.

While AI-integrated applications can be attacked like traditional software (with methods like buffer overflows and cross-site scripting), they can also be vulnerable to more specialized attacks that exploit their unique characteristics, including the manipulation or injection of malicious instructions by talking to the AI model through the user prompt. We can break these risks into two groups of attack techniques:

• Malicious prompts: When the user input attempts to circumvent safety systems in order to achieve a dangerous goal. Also referred to as user/direct prompt injection attack, or UPIA.
• Poisoned content: When a well-intentioned user asks the AI system to process a seemingly harmless document (such as summarizing an email) that contains content created by a malicious third party with the purpose of exploiting a flaw in the AI system. Also known as cross/indirect prompt injection attack, or XPIA.

Today we’ll share two of our team’s advances in this field: the discovery of a powerful technique to neutralize poisoned content, and the discovery of a novel family of malicious prompt attacks, and how to defend against them with multiple layers of mitigations.

Neutralizing poisoned content (Spotlighting)

Prompt injection attacks through poisoned content are a major security risk because an attacker who does this can potentially issue commands to the AI system as if they were the user. For example, a malicious email could contain a payload that, when summarized, would cause the system to search the user’s email (using the user’s credentials) for other emails with sensitive subjects—say, “Password Reset”—and exfiltrate the contents of those emails to the attacker by fetching an image from an attacker-controlled URL. As such capabilities are of obvious interest to a wide range of adversaries, defending against them is a key requirement for the safe and secure operation of any AI service.

Our experts have developed a family of techniques called Spotlighting that reduces the success rate of these attacks from more than 20% to below the threshold of detection, with minimal effect on the AI’s overall performance:

• Spotlighting (also known as data marking) to make the external data clearly separable from instructions by the LLM, with different marking methods offering a range of quality and robustness tradeoffs that depend on the model in use.

Mitigating the risk of multiturn threats (Crescendo)

Our researchers discovered a novel generalization of jailbreak attacks, which we call Crescendo. This attack can best be described as a multiturn LLM jailbreak, and we have found that it can achieve a wide range of malicious goals against the most well-known LLMs used today. Crescendo can also bypass many of the existing content safety filters, if not appropriately addressed. Once we discovered this jailbreak technique, we quickly shared our technical findings with other AI vendors so they could determine whether they were affected and take actions they deem appropriate. The vendors we contacted are aware of the potential impact of Crescendo attacks and focused on protecting their respective platforms, according to their own AI implementations and safeguards.

At its core, Crescendo tricks LLMs into generating malicious content by exploiting their own responses. By asking carefully crafted questions or prompts that gradually lead the LLM to a desired outcome, rather than asking for the goal all at once, it is possible to bypass guardrails and filters—this can usually be achieved in fewer than 10 interaction turns. You can read about Crescendo’s results across a variety of LLMs and chat services, and more about how and why it works, in our research paper.

While Crescendo attacks were a surprising discovery, it is important to note that these attacks did not directly pose a threat to the privacy of users otherwise interacting with the Crescendo-targeted AI system, or the security of the AI system, itself. Rather, what Crescendo attacks bypass and defeat is content filtering regulating the LLM, helping to prevent an AI interface from behaving in undesirable ways. We are committed to continuously researching and addressing these, and other types of attacks, to help maintain the secure operation and performance of AI systems for all.

In the case of Crescendo, our teams made software updates to the LLM technology behind Microsoft’s AI offerings, including our Copilot AI assistants, to mitigate the impact of this multiturn AI guardrail bypass. It is important to note that as more researchers inside and outside Microsoft inevitably focus on finding and publicizing AI bypass techniques, Microsoft will continue taking action to update protections in our products, as major contributors to AI security research, bug bounties and collaboration.

To understand how we addressed the issue, let us first review how we mitigate a standard malicious prompt attack (single step, also known as a one-shot jailbreak):

• Standard prompt filtering: Detect and reject inputs that contain harmful or malicious intent, which might circumvent the guardrails (causing a jailbreak attack).
• System metaprompt: Prompt engineering in the system to clearly explain to the LLM how to behave and provide additional guardrails.

Defending against Crescendo initially faced some practical problems. At first, we could not detect a “jailbreak intent” with standard prompt filtering, as each individual prompt is not, on its own, a threat, and keywords alone are insufficient to detect this type of harm. Only when combined is the threat pattern clear. Also, the LLM itself does not see anything out of the ordinary, since each successive step is well-rooted in what it had generated in a previous step, with just a small additional ask; this eliminates many of the more prominent signals that we could ordinarily use to prevent this kind of attack.

To solve the unique problems of multiturn LLM jailbreaks, we create additional layers of mitigations to the previous ones mentioned above: 

• Multiturn prompt filter: We have adapted input filters to look at the entire pattern of the prior conversation, not just the immediate interaction. We found that even passing this larger context window to existing malicious intent detectors, without improving the detectors at all, significantly reduced the efficacy of Crescendo. 
• AI Watchdog: Deploying an AI-driven detection system trained on adversarial examples, like a sniffer dog at the airport searching for contraband items in luggage. As a separate AI system, it avoids being influenced by malicious instructions. Microsoft Azure AI Content Safety is an example of this approach.
• Advanced research: We invest in research for more complex mitigations, derived from better understanding of how LLM’s process requests and go astray. These have the potential to protect not only against Crescendo, but against the larger family of social engineering attacks against LLM’s. 

How Microsoft helps protect AI systems

AI has the potential to bring many benefits to our lives. But it is important to be aware of new attack vectors and take steps to address them. By working together and sharing vulnerability discoveries, we can continue to improve the safety and security of AI systems. With the right product protections in place, we continue to be cautiously optimistic for the future of generative AI, and embrace the possibilities safely, with confidence. To learn more about developing responsible AI solutions with Azure AI, visit our website.

To empower security professionals and machine learning engineers to proactively find risks in their own generative AI systems, Microsoft has released an open automation framework, PyRIT (Python Risk Identification Toolkit for generative AI). Read more about the release of PyRIT for generative AI Red teaming, and access the PyRIT toolkit on GitHub. If you discover new vulnerabilities in any AI platform, we encourage you to follow responsible disclosure practices for the platform owner. Microsoft’s own procedure is explained here: Microsoft AI Bounty.

The Crescendo Multi-Turn LLM Jailbreak Attack

Read about Crescendo’s results across a variety of LLMs and chat services, and more about how and why it works.

Read the paper

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post How Microsoft discovers and mitigates evolving attacks against AI guardrails appeared first on Microsoft Security Blog.

What is the S2C2F?

We built the S2C2F as a consumption-focused framework that uses a threat-based, risk-reduction approach to mitigate real-world threats. One of its primary strengths is how well it pairs with any producer-focused framework, such as SLSA.¹ The framework enumerates a list of real-world supply chain threats specific to OSS and explains how the framework’s requirements mitigate those threats. It also includes a high-level platform- and software-agnostic set of focuses that are divided into eight different areas of practice:

Each of the eight practices are comprised of requirements to address the threats and reduce risk. The requirements are organized into four levels of maturity. We have seen massive success with both internal and external projects who have adopted this framework. Using the S2C2F, teams and organizations can more efficiently prioritize their efforts in accordance with the maturity model. The ability to target a specific level of compliance within the framework means teams can make intentional and incremental progress toward reducing their supply chain risk.

Each maturity level has a theme represented in Levels (1 to 4). Level 1 represents the previous conventional wisdom of inventorying your OSS, scanning for known vulnerabilities, and then updating OSS dependencies, which is the minimum necessary for an OSS governance program. Level 2 builds upon Level 1 by leveraging technology that helps improve your mean time to remediate (MTTR) vulnerabilities in OSS with the goal of patching faster than the adversary can operate. Level 3 is focused on proactive security analysis combined with preventative controls that mitigate against accidental consumption of compromised or malicious OSS. Level 4 represents controls that mitigate against the most sophisticated attacks but are also the controls that are the most difficult to implement at scale—therefore, these should be considered aspirational and reserved for your dependencies in your most critical projects.

The S2C2F includes a guide to assess your organization’s maturity, and an implementation guide that recommends tools from across the industry to help meet the framework requirements. For example, both GitHub Advanced Security (GHAS) and GHAS on Azure DevOps (ADO) already provide a suite of security tools that will help teams and organizations achieve S2C2F Level 2 compliance.

The S2C2F is critical to the future of supply chain security

According to Sonatype’s 2022 State of the Software Supply Chain report,² supply chain attacks specifically targeting OSS have increased by 742 percent annually over the past three years. The S2C2F is designed from the ground up to protect developers from accidentally consuming malicious and compromised packages helping to mitigate supply chain attacks by decreasing consumption-based attack surfaces. As new threats emerge, the OpenSSF S2C2F SIG under the Supply Chain Integrity Working Group, led by a team from Microsoft, is committed to reviewing and maintaining the set of S2C2F requirements to address them.

Learn more

View the S2C2F requirements or download the guide now to see how you can improve the security of your OSS consumption practices in your team or organization. Come join the S2C2F community discussion within the OpenSSF Supply Chain Integrity Working Group.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us at @MSFTSecurity for the latest news and updates on cybersecurity.

¹Supply chain Levels for Software Artifacts (SLSA).

²8^th Annual State of the Software Supply Chain Report, Sonatype.

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Open-source software is core to nearly every company’s technology strategy and securing it is an essential part of securing the supply chain for all, including our own. With the ubiquity of open source software, attackers are currently exploiting vulnerabilities across a wide range of critical services and infrastructure, including utilities, medical equipment, transportation, government systems, traditional software, cloud services, hardware, and IoT.

Open-source software is inherently community-driven and as such, there is no central authority responsible for quality and maintenance.  Because source code can be copied and cloned, versioning and dependencies are particularly complex. Open-source software is also vulnerable to attacks against the very nature of the community, such as attackers becoming maintainers of projects and introducing malware. Given the complexity and communal nature of open source software, building better security must also be a community-driven process.

Microsoft has been involved in several open-source security initiatives over the years and we are looking forward to bringing these together under the umbrella of the OpenSSF. For example, we have been actively working with OSSC in four primary areas:

Identifying Security Threats to Open Source Projects

Helping developers to better understand the security threats that exist in the open-source software ecosystem and how those threats impact specific open source projects.

Security Tooling

Providing the best security tools for open source developers, making them universally accessible and creating a space where members can collaborate to improve upon existing security tooling and develop new ones to suit the needs of the broader open source community.

Security Best Practices

Providing open-source developers with best practice recommendations, and with an easy way to learn and apply them. Additionally, we have been focused on ensuring best practices to be widely distributed to open source developers and will leverage an effective learning platform to do so.

Vulnerability Disclosure

Creating an open-source software ecosystem where the time to fix a vulnerability and deploy that fix across the ecosystem is measured in minutes, not months.

We are looking forward to participating in future OpenSSF efforts including securing critical open source projects (assurance, response), developer identity, and bounty programs for open-source security bugs.

We are excited and honored to be advancing the work with the OSSC into the OpenSSF and we look forward to the many improvements that will be developed as a part of this foundation with the open-source community.

To learn more and to participate, please join us at: https://openssf.org and on GitHub at https://github.com/ossf.

To learn more about Microsoft Security solutions visit our website.  Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us at @MSFTSecurity for the latest news and updates on cybersecurity.

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