Indirect prompt injection (IPI) is an evolving threat vector targeting users of complex AI applications with multiple data sources, such as Workspace with Gemini. This technique enables the attacker to influence the behavior of an LLM by injecting malicious instructions into the data or tools used by the LLM as it completes the user’s query. This may even be possible without any input directly from the user.
IPI is not the kind of technical problem you “solve” and move on. Sophisticated LLMs with increasing use of agentic automation combined with a wide range of content create an ultra-dynamic and evolving playground for adversarial attacks. That’s why Google takes a sophisticated and comprehensive approach to these attacks. We’re continuously improving LLM resistance to IPI attacks and launching AI application capabilities with ever-improving defenses. Staying ahead of the latest indirect prompt injection attacks is critical to our mission of securing Workspace with Gemini.
In our previous blog “Mitigating prompt injection attacks with a layered defense strategy”, we reviewed the layered architecture of our IPI defenses. In this blog, we’ll share more detail on the continuous approach we take to improve these defenses and to solve for new attacks.
New attack discovery
By proactively discovering and cataloging new attack vectors through internal and external programs, we can identify vulnerabilities and deploy robust defenses ahead of adversarial activity.
Human Red-Teaming
Human Red-Teaming uses adversarial simulations to uncover security and safety vulnerabilities. Specialized teams execute attacks based on realistic user profiles to exploit weaknesses, coordinating with product teams to resolve identified issues.
Automated Red-Teaming
Automated Red-Teaming is done via dynamic, machine-learning-driven frameworks to stress-test environments. By algorithmically generating and iterating on attack payloads, we can mimic the behavior of sophisticated threats at scale. This allows us to map complex attack paths and validate the effectiveness of our security controls across a much wider range of edge cases than manual testing could achieve on its own.
Google AI Vulnerability Rewards Program (VRP)
The Google AI Vulnerability Rewards Program (VRP) is a critical tool for enabling collaboration between Google and external security researchers who discover new attacks leveraging IPI. Through this VRP, we recognize and reward contributors for their research. We also host regular, live hacking events where we provide invited researchers access to pre-release features, proactively uncovering novel vulnerabilities. These partnerships enable Google to quickly validate, reproduce, and resolve externally-discovered issues.
Publicly disclosed AI attacks
Google utilizes open-source intelligence feeds to stay on top of the latest publicly disclosed IPI attacks, across social media, press releases, blogs, and more. From there, new AI vulnerabilities are sourced, reproduced, and catalogued internally to ensure our products are not impacted.
Vulnerability catalog
All newly discovered vulnerabilities go through a comprehensive analysis process performed by the Google Trust, Security, & Safety teams. Each new vulnerability is reproduced, checked for duplications, mapped into attack technique / impact category, and assigned to relevant owners. The combination of new attack discovery sources and vulnerability catalog process helps Google stay on top of the latest attacks in an actionable manner.
Synthetic data generation
After we discover, curate, and catalog new attacks, we use Simula to generate synthetic data expanding these new attacks. This process is essential because it allows the team to develop attack variants for completeness and coverage, and to prepare new training and validation data sets. This accelerated workflow has boosted synthetic data generation by 75%, supporting large-scale defense model evaluation and retraining, as well as updating the data set used for calculating and reporting on defense effectiveness.
Ongoing defense refinement
Continually updating and enhancing our defense mechanisms allows us to address a broader range of attack techniques, effectively reducing the overall attack surface. Updating each defense type requires different tasks, from config updates, to prompt engineering and ML model retraining.
Deterministic Defenses
Deterministic defenses, including user confirmation, URL sanitization, and tool chaining policies, are designed for rapid response against new or emerging prompt injection attacks by relying on simple configuration updates. These defenses are governed by a centralized Policy Engine, with configurations for policies like baseline tool calls, URL sanitization, and tool chaining. For immediate threats, this configuration-based system facilitates a streamlined process for "point fixes," such as regex takedowns, providing an agile defense layer that acts faster than traditional ML/LLM model refresh cycles.
ML-Based Defenses
After generating synthetic data that expands new attacks into variants, the next step is to retrain our ML-based defenses to mitigate these new attacks. We partition the synthetic data described above into separate training and validation sets to ensure performance is evaluated against held-out examples. This approach ensures repeatability, data consistency for fixed training/testing, and establishes a scalable architecture to support future extensions towards fully automated model refresh.
LLM-Based Defenses
Using the new synthetic data examples, our LLM-based defenses go through prompt engineering with refined system instructions. The goal is to iteratively optimize these prompts against agreed-upon defense effectiveness metrics, ensuring the models remain resilient against evolving threat vectors.
Gemini Model Hardening
Beyond system-level guardrails and application-level defenses, we prioritize ‘model hardening’, a process that improves the Gemini model's internal capability to identify and ignore harmful instructions within data. By utilizing synthetic datasets and fresh attack patterns, we can model various threat iterations. This enables us to strengthen the Gemini model's ability to disregard harmful embedded commands while following the user's intended request. Through this process of model hardening, Gemini has become significantly more adept at detecting and disregarding injected instructions. This has led to a reduction in the success rate of attacks without compromising the model's efficiency during routine operations.
Defense effectiveness
To measure the real-world impact of defense improvements, we simulate attacks against many Workspace features. This process leverages the newly generated synthetic attack data described on this blog, to create a robust, end-to-end evaluation. The simulation is run against multiple Workspace apps, such as Gmail and Docs, using a standardized set of assets to ensure reliable results. To determine the exact impact of a defense improvement (e.g., an updated ML model or a new LLM prompt optimization), the end-to-end evaluation is run with and without the defense enabled. This comparative testing provides the essential "before and after" metrics needed to validate defense efficacy and drive continuous improvement.
Moving forward
Our commitment to AI security is rooted in the principle that every day you’re safer with Google. While the threat landscape of indirect prompt injection evolves, we are building Workspace with Gemini to be a secure and trustworthy platform for AI-first work. IPI is a complex security challenge, which requires a defense-in-depth strategy and continuous mitigation approach. To get there, we’re combining world-class security research, automated pipelines, and advanced ML/LLM-based models. This robust and iterative framework helps to ensure we not only stay ahead of evolving threats but also provide a powerful, secure experience for both our users and customers.
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Facts Only
Google identifies indirect prompt injection (IPI) as an evolving threat targeting AI applications like Workspace with Gemini.
IPI involves embedding malicious instructions in data or tools used by LLMs, potentially without direct user input.
Google uses human red-teaming, where specialized teams simulate attacks based on realistic user profiles.
Automated red-teaming employs machine-learning frameworks to generate and test attack payloads at scale.
The Google AI Vulnerability Rewards Program (VRP) incentivizes external researchers to discover and report IPI vulnerabilities.
Publicly disclosed AI attacks are monitored via open-source intelligence and cataloged internally.
Discovered vulnerabilities undergo analysis by Google’s Trust, Security, & Safety teams for duplication, categorization, and assignment.
Synthetic data generation, using tools like Simula, expands attack variants for training and validation, increasing output by 75%.
Deterministic defenses include user confirmation, URL sanitization, and tool chaining policies, managed via a centralized Policy Engine.
ML-based defenses are retrained using partitioned synthetic data to evaluate performance against held-out examples.
LLM-based defenses undergo prompt engineering to optimize resilience against evolving threats.
Gemini model hardening improves the model’s ability to detect and ignore harmful instructions while maintaining operational efficiency.
Defense effectiveness is measured through end-to-end simulations across Workspace apps like Gmail and Docs.
Google frames its approach as part of a continuous, defense-in-depth strategy to address the dynamic nature of IPI threats.
Executive Summary
Indirect prompt injection (IPI) represents an emerging threat to AI systems like Google's Workspace with Gemini, where attackers manipulate LLM behavior by embedding malicious instructions in data sources rather than direct user input. Google employs a multi-layered defense strategy, combining human and automated red-teaming, vulnerability rewards programs, and open-source intelligence to proactively identify and mitigate new attack vectors. Their approach includes deterministic defenses (e.g., URL sanitization), ML-based defenses (retrained models using synthetic attack data), and LLM-based defenses (refined system prompts). Model hardening enhances Gemini's ability to detect and ignore harmful instructions while preserving functionality. Google measures defense effectiveness through end-to-end simulations across Workspace apps, ensuring continuous improvement. The company emphasizes that IPI is an evolving challenge requiring persistent, adaptive security measures rather than a one-time fix.
The strategy integrates internal research, external collaboration via the AI Vulnerability Rewards Program, and synthetic data generation to expand attack coverage. Google's commitment to AI security is framed as part of its broader mission to provide a secure, trustworthy platform for AI-driven work, acknowledging the dynamic nature of adversarial threats in complex AI ecosystems.
Full Take
**Steelman:** Google’s approach to mitigating indirect prompt injection (IPI) is robust, combining proactive threat discovery, layered defenses, and continuous improvement. By integrating human expertise, automated testing, and external collaboration, they demonstrate a commitment to staying ahead of adversarial tactics. The use of synthetic data to expand attack coverage and the emphasis on model hardening reflect a sophisticated understanding of AI security challenges. Their transparency about the evolving nature of IPI—rather than claiming a permanent solution—adds credibility to their strategy.
**Pattern Scan:** The narrative leans heavily on **authority games** (ARC-0012), framing Google’s multi-layered defenses as the gold standard without independent verification of their effectiveness. The repeated emphasis on "continuous improvement" and "evolving threats" could border on **mission drift** (ARC-0031), where the lack of a definitive solution is positioned as a feature rather than a limitation. However, the absence of emotional exploitation or forced binaries suggests a largely good-faith effort to inform rather than manipulate.
**Root Cause:** The paradigm here is one of **security as an arms race**, where defenders must perpetually adapt to adversarial innovation. The unstated assumption is that AI systems like Gemini can be made "secure enough" through iterative hardening, despite the inherent unpredictability of LLMs. This echoes historical patterns in cybersecurity, where defenses are always playing catch-up to offensive techniques.
**Implications:** For human agency, the reliance on automated defenses and model hardening could reduce user visibility into how decisions are made, potentially eroding trust if failures occur. The costs are borne by Google’s security teams and external researchers, while the benefits accrue to users—but only if the defenses hold. Second-order consequences include the risk of over-reliance on synthetic data, which may not capture all real-world attack vectors, and the potential for adversaries to reverse-engineer defenses from public disclosures.
**Bridge Questions:** How might Google’s centralized Policy Engine introduce single points of failure? What trade-offs exist between defense agility and model interpretability? If IPI attacks become more sophisticated, could the current layered approach scale, or would fundamental architectural changes be needed?
**Counterstrike Scan:** A coordinated influence campaign might exaggerate the effectiveness of Google’s defenses while downplaying limitations, using jargon to obscure gaps. The actual content, however, aligns more with a technical brief than a manipulative playbook—it acknowledges challenges and avoids overpromising. No structural alignment with a hypothetical attack pattern is detected.
Sentinel — Human
The text exhibits human-like qualities such as variable sentence length, passionate emphasis, and diverse use of vocabulary. However, it also includes technical details that might suggest some AI assistance in the research or editing process.