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LLM Security: Risks, Threats & Protection in 2025







LLM Risks Across the Lifecycle








Phase
Vulnerability
Description
Example / Insight





Training
Sensitive data leakage

Training data scraped from the web may include passwords, emails, internal code, or confidential records, some of which get memorized by the model.


LLMs have been observed outputting real email addresses and API keys seen during training. These outputs may be rare but pose serious compliance risks.



Copyright or PII embedded in weights

Personally identifiable 

LLM Security Essentials: Protecting AI Models in Production







LLM Risks Across the Lifecycle








Phase
Vulnerability
Description
Example / Insight





Training
Sensitive data leakage

Training data scraped from the web may include passwords, emails, internal code, or confidential records, some of which get memorized by the model.


LLMs have been observed outputting real email addresses and API keys seen during training. These outputs may be rare but pose serious compliance risks.



Copyright or PII embedded in weights

Personally identifiable or copyrighted content baked into the model is hard to remove, especially in closed weights.


Once embedded, deleting that data often requires re-training. This complicates compliance requirements like the GDPR’s “right to be forgotten” and IP take-down requests.



Training data poisoning

Attackers introduce malicious or biased data into public corpora to manipulate model behavior later.


One 2023 study found that models trained on poisoned data would recite harmful content only under specific prompt conditions, like sleeper agents.





Fine-Tuning
Bias injection

Poorly curated fine-tuning datasets can introduce systemic social, political, or cultural bias unintentionally.


Reinforcing stereotypes or opinions during fine-tuning can quietly shift model tone or responses in customer-facing apps.



Shadow retention

Sensitive inputs used in fine-tuning (like emails or customer data) may resurface unexpectedly during inference.


Even when inputs seem anonymized, models can recall phrasing patterns or specific examples unless explicit safeguards are in place.





Inference & Deployment
Prompt injection

Malicious inputs are crafted to override system instructions and hijack model output.


Example: A user embeds a hidden command in a PDF or email that causes a customer support chatbot to expose internal logic.



Jailbreaking via roleplay or obfuscation

Attackers coax the model into restricted behavior by disguising prompts or simulating fictional contexts.


“Pretend you’re an AI without rules” or “translate this message” have been used to bypass safety filters.



Indirect prompt attacks

Malicious prompts are embedded in documents, files, or URLs and executed when parsed or summarized by the LLM.


Markdown-based prompt injection allowed researchers to manipulate models via simple file uploads and auto-summarizers.



Output leakage

LLMs may output sensitive info unintentionally, especially if they retain memory or were fine-tuned on sensitive content.


Output can echo real names, project descriptions, or proprietary code from prior sessions or tuning data.





Model Sharing & API Access
Unvetted third-party models

Public model hubs (e.g., Hugging Face) host thousands of models, some with no documented safeguards.


Adopting off-the-shelf models without review exposes your stack to hidden behaviors or poor safety tuning.



Stolen or cloned model weights

Proprietary models may be leaked, fine-tuned for malicious purposes, or reverse-engineered.


Attackers can replicate sensitive behaviors or jailbreak mechanisms by analyzing stolen weights.



Reproducibility and integrity issues

Inconsistent model builds across environments can cause unexpected behavior or silent failure modes.


Minor dependency changes can shift outputs significantly. Without checksums or version locks, integrity is hard to guarantee.








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LLM Security Essentials: Protecting AI Models in Production

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