# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 2 Background  
### Security and Privacy Challenges in LLMs  
LLMs are exposed to the following forms of security threats, among others:  
* **Model inversion attacks:** Attackers can learn/reconstruct/exfiltrate training data via exposed parameters [12];
* **Membership inference attacks:** These enable attackers to determine whether any specific record of data has contributed to the training process [13];
* **Evasion attacks via prompt engineering:** Malicious users manipulate vulnerabilities in the prompt to trick content filters into showing sensitive behaviors [14]. Recent studies indicate that modifications using adversarial prompt engineering result in a more sophisticated and intricate form of attack, utilizing a prompt-based methodology that either diminishes the model's performance or compels it to disclose private training data [15]. Moreover, mass scraping of inference-time user inputs has created large-scale vulnerabilities to new privacy hazards because sensitive personal or corporate information may be cached or shared at inference [16]. Indeed, as LLMs' parameters grow in size, along with the training corpora, more attack surfaces open to adversaries or malicious actors, multiplying the exposure to leakages or unauthorized model reuse [17]. Another area of growing concern is the unintentional memorization of rare or unique examples in training, which can lead to privacy leakages if models reproduce these examples on request [18]. This is especially sensitive in domains like health and finance, where even the unintentional short-term exposure of personally identifiable information (PII) can be illegal and unethical [19]. Recent research has shown that despite fine-tuning and filtering mechanisms, LLMs can generate recognizable segments of text when given adversarial prompts, particularly when the original training dataset originates from uncurated, extensive internet archives. With the proliferation of public large language model APIs, researchers have also discovered new vectors for indirect data harvesting. Attackers can exploit application integrations--such as plugins or software libraries that connect LLMs to external data sources--to stage complex data extraction campaigns [20]. Moreover, the increasing sophistication of membership inference attacks has led to \"label-only\" or \"black-box\" methods that do not require extensive knowledge of the model internals [21]. These threats raise considerable alarm for organizations that rely on LLM-based solutions for critical tasks or that handle personal health records (PHRs), financial documents, or intellectual property. Recent work has also highlighted the challenge of \"prompt chaining\", wherein an attacker crafts multiple queries that leverage partial information revealed in earlier responses [22]. Over time, such queries may accumulate enough fragments to reconstruct confidential data. In practice, distinguishing benign queries from hostile ones can be subtle, particularly where user interactions are not tightly controlled. Security teams are thus exploring user-level audit trails, more sophisticated anomaly detection, and finer-grained content filters as countermeasures against data exfiltration using sequential queries. An additional concern is the potential for models to be misused to generate disinformation or harmful content. While it is not always classified as a direct privacy leakage, the generation of disinformation can be considered a security threat in contexts where social engineering or phishing attacks leverage LLM-generated text [23]. Adversaries can exploit a model to produce highly personalized spam-phishing messages that mimic a person's language style, extracted from scattered, user-specific information inadvertently revealed through the massive language model. In short, such vulnerabilities require strong security protocols to secure the training data, maintain user anonymity at inference, and monitor any potential interactions with hostile entities.