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Your model's guardrails work well. Your output filters do too. But the real attack vector, the context window, is almost never included in your threat model.
Context engineering security risks are not theoretical. They exist in real production systems right now. Context engineering is about deciding what your model sees. When done poorly, it does more than lower output quality. It creates four clear vulnerabilities that attackers are already exploiting. In my work with engineering teams in regulated industries, these are the gaps I see most often, and they cause the most damage when ignored.
Why the context window is your real security perimeter
Every production LLM application builds a context window before each response. This includes system prompts, retrieved documents, tool outputs, conversation history, and memory. The model processes all of it the same way, with no internal boundary between trusted instructions and untrusted external content.
The UK's National Cyber Security Centre addressed this directly in December 2025. Its technical director for platforms research published a formal assessment, available at ncsc.gov.uk, concluding that prompt injection may never be fully fixed the way SQL injection was. This is because LLMs do not separate data and instructions at the architecture level. OWASP has ranked prompt injection #1 in its LLM Top 10 for two years running. In almost every documented case, the root cause is the same: context that was not designed with security in mind.
The four context engineering risks hiding in your AI stack
These are not edge cases. Each of these AI security vulnerabilities has appeared in real production systems in 2025 and 2026.
1. Context poisoning via RAG pipelines
Attackers can hide malicious instructions inside a document that your ingestion pipeline collects. When that document is retrieved, those instructions enter the context window along with your real content. The model processes both as if they are the same.
What makes this dangerous:
- The attack surface is your ingestion pipeline, not the chat interface your security team is watching.
- Poisoned documents look legitimate in meaning, so output filters cannot detect them.
- Defending at inference time is too late. The right control is at the ingestion stage.
This is one of the clearest examples of data poisoning in AI that engineering teams consistently underestimate.
2. Context stuffing and the attention gaps it opens for attackers
Loading everything into the context window — raw tool outputs, full document collections, unfiltered session history — creates both performance and security problems at the same time.
Studies confirm every model shows accuracy degradation as context grows. When context is bloated, critical safety instructions get pushed toward the middle of the window where model attention is weakest. Researchers call this "lost in the middle" degradation.
An attacker who understands this does not need to break your guardrails. They just need to bury them.
3. AI data leakage through the context window
Production context windows routinely carry material that should never be exposed:
- API credentials and internal routing logic embedded in system prompts
- Internal documents retrieved via RAG without per-user access controls
- PII and session data accumulated across multi-turn conversations
OWASP elevated sensitive information disclosure from #6 to #2 in its 2025 LLM Top 10 update because of precisely how frequently this occurs in production. AI data leakage risks tied to the context window are now a top compliance concern, not just a security one.
4. Prompt injection via multi-turn context manipulation
In multi-turn systems, conversation history is controlled by the user. An attacker does not need a single complex prompt. They can use early turns in a session to plant instructions that persist through later turns.
Johann Rehberger demonstrated this with Gemini Advanced in February 2025. By manipulating context across turns, he corrupted the AI's long-term memory. False information persisted across sessions until it was manually removed. The model itself was not broken. The problem was context design.
This is a direct example of context injection attacks in production — and a clear illustration of why multi-turn context needs its own security controls.
How to fix context engineering risks before they become incidents
Here is exactly what responsible context engineering looks like as a security practice.
- Defend at ingestion, not at inference. Validate documents before they enter your knowledge base, not after retrieval. Flag instruction-like language — commands or phrases that override roles — at the ingestion stage. Apply access controls at the retrieval layer so the model only surfaces documents a specific user is authorised to see.
- Treat context as a budget with security risks. Load only what the current task needs, not everything available. Place important safety instructions at both the beginning and end of the context, not just at the top. Limit tool permissions to the current task, not the whole session.
- Log context, not just outputs. Most AI observability tools log model outputs, but that is not enough. Log the full context payload at inference time — what the model saw before generating its response. This is your forensic record. Without it, you cannot diagnose context-level failures or prove compliance if something goes wrong.
The compliance exposure context attacks create directly
Context engineering security risks are not an AI safety concern. They are a data protection and compliance concern.
A context attack that leaks or manipulates personal data is a data breach. This triggers notification duties, potential fines, and full regulatory exposure. These risks now map directly to seven frameworks: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF 1.0, EU AI Act, ISO 42001, GDPR, and NIS2. The EU AI Act's enforcement deadline is August 2026.
If context injection is not in your threat register or your vendor security assessments, it needs to be.
The honest assessment, and what to do with it
The security posture of your AI system is not primarily determined by the model you chose or the vendor's guardrails. It is determined by the quality of your context engineering — what enters the context window, from which sources, under what access controls, and with what validation at every stage.
In my experience, teams that treat context as infrastructure — design it carefully, monitor it continuously, and govern it deliberately — are the ones who avoid incidents that others learn about the hard way.
At Opcito, our AI/ML and data engineering practice works directly with engineering teams on this layer — from secure RAG architecture and context design to agentic pipeline reviews ahead of regulated production deployments. If you want a second set of eyes on your AI architecture before an incident forces that conversation, our team is available.
