As artificial intelligence continues its rapid evolution, the emergence of agentic AI—autonomous systems capable of making decisions, executing tasks, and adapting dynamically to context—marks a profound shift in the capabilities and architectures of modern software.
These agents aren’t just passive engines responding to prompts; they act, plan, interact, and improve based on feedback.
But with this power comes a new array of security concerns, particularly surrounding one of the most underappreciated and emerging vulnerabilities: memory attacks.
In general, hackers look for design errors and system/memory
vulnerabilities to inject malware into networks or systems.
Last week at the Gartner Security & Risk Management Summit, which took place in National Harbor, MD, and spans 3 days, presenters covered strategies to address concerns for the industry’s hottest topics, including Agentic AI, and in this article, we will be taking a brief look at suggestions from the summit.
‘Hyped’ GenAI And Associated Cybersecurity Risks
“There are two broad classifications of memory attacks. The first involves attacks on storage devices that are used to boot or load an operating system or software for a machine…often, but not always, these require physical access to the machine to mount an effective attack on the storage. The second involves RAM devices that store temporary data. These devices are more likely to be attacked through the machine itself, including through internet-connected attacks. Physical attacks on RAM are also a possibility. – source.
As previously mentioned, this new layer of intelligence—memory—is also a new attack surface in many different ways.
1. Memory Poisoning Attacks
Just as traditional machine learning systems are vulnerable to data poisoning, agentic AIs can be fed carefully crafted inputs that, once stored in memory, alter future behavior. For instance, a bad actor could inject misinformation into an AI assistant’s memory through interactions that appear benign, leading it to act incorrectly or even maliciously later on.
As an example:
- A customer service AI might be tricked into remembering a false refund policy.
- A developer agent might be led to store and reuse insecure code snippets.
- A personal assistant could be manipulated to “recall” fake events or contacts, altering user behavior.
Because these memories are often stored as vector embeddings or text fragments, their validation and sanitization become difficult, especially in real-time environments.
2. Memory Hijacking and Exfiltration
In some AI implementations, memory is stored in external vector databases or serialized on disk. If improperly secured, these memory stores become valuable targets for attackers. Hijacking memory could mean:
- Exfiltrating sensitive user data (past queries, summaries, plans).
- Injecting backdoors into a model’s contextual reasoning.
- Creating long-term surveillance via «invisible» entries—inputs the user never sees, but the AI does.
Memory hijacking essentially turns the agent into a sleeper cell: compromised now but quietly storing altered data to be potentially weaponized later.
3. Temporal Manipulation
Agentic AIs often rely on timelines, event logs, or planning sequences. Malicious actors can attempt temporal manipulation—tricking agents into misunderstanding the order of events, which could result in skipped actions, repeated steps, or even undesired escalations.
In coordinated system attacks, this could be used to disrupt sequences in customer support, logistics, or even automated DevOps tasks.
Tackling Security Challenges Unique to Agentic AI
Although traditional cybersecurity practices—firewalls, role-based access control, encryption—are essential, they alone are often insufficient in protecting the internal logic and evolving behavior of AI agents since they have very unique challenges, including but not limited to:
1. Opaque Decision Trails
The reasoning path an agent takes may involve thousands of tokens and dynamically retrieved data from memory, making auditing and debugging very difficult. If an agent makes a mistake or acts suspiciously, it’s not always clear why.
2. Dynamic Tool Use
Agents often integrate tool use (e.g., calling APIs, sending emails, writing to databases). This ability makes them powerful, but also dangerous if hijacked. A single poisoned memory can lead to broad system-level effects if the agent has access to critical functions.
3. Impersonation and Social Engineering
If memory isn’t scoped correctly per user or domain, it opens the door for cross-user contamination. A malicious user might insert prompts or data that later affect another user’s agent session—an AI version of a cross-site scripting (XSS) attack.
Toward a Secure Agentic Future
During the opening keynote of the Summit, Gartner associates Katell Thielemann and Leigh McMullen outlined three key strategies to help anticipate the future needs of CISOs and allow them to meet the needs of today’s complex, fast, and unpredictable cybersecurity reality.
“Leaders aren’t just placing bets on GenAI and other explorative technology; they’re also concerned about the cybersecurity risks associated with them.” —Katell Thielemann, VP Analyst at Gartner.
These three areas include being mission-aligned, innovation-ready, and change-agile. Although interest in agentic AI is surging despite security concerns, for instance, a recent Gartner poll revealed 24% of CIOs and IT leaders have deployed AI agents, and more than 50% are researching or experimenting with the technology, nonetheless:
- Thielemann believes that CISOs must prove that their cybersecurity efforts are aligned with their organization’s mission by transparently showing how cyber investment decisions and exposure implications correlate.
To achieve this, she believes CISOs must start by identifying outcome-driven metrics (ODMs), or metrics that measure the current level of cybersecurity protection and exposure.
- Additionally, CISOs should explore three steps to enable their organization’s longer-term AI ambitions, including
- Cultivating AI literacy for themselves and their teams.
- Experimenting with AI in cybersecurity, from code analysis to threat hunting and modeling, to user behavior analysis.
- Protect AI investments in their organizations by taking actions such as revising data retention policies to protect prompts, input, and output storage; implementing comprehensive risk assessments for custom-built GenAI; and carrying out regulatory compliance audits.
- Further, in an era where employees are increasingly change resistant and even fearful of AI, CISOs must be on the lookout for burnout from their employees, says Thielemann, whether that is through unexpected surprises, a feeling of lack of agency, or via boring, repetitive tasks.
According to McMullen, “CISOS must be able to empower their teams to be part of the solution and feel agency,”. “If CISOs’ teams feel agency, they will want to focus on automating repetitive tasks and developing new skills to fuel your growth as well as theirs, which in turn will make them resilient agents of change no matter what that change is.”
To prepare for this new cybersecurity frontier, in particular, AI and security teams must co-design systems with memory integrity and agentic transparency as core principles, including encoding proactive strategies that might include:
- Memory Isolation: AI and cybersecurity teams may want to segment memory per user, per context, and per domain to prevent contamination or leakage.
- Memory Validation and Redaction: Teams may want to use trusted logic or fine-tuned models to vet what gets stored.
- Agent Audit Logs: They may want to keep immutable logs of agent decisions and memory access, ideally with natural-language summaries for transparency.
- Tool Use Constraints: Further, it might be better to apply dynamic sandboxes or rule-based approvals before agents can call sensitive tools or APIs.
- Behavioral Baselines and Drift Detection: Finally, they may benefit from monitoring agent behavior over time to detect unexpected changes that could result from compromised memory or inputs.
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