LLM Attack Evolution

5 automated security scanners

Purpose: This scanner detects and assesses the presence of LLM (Large Language Model)-optimized social engineering content targeting organizations. It identifies indicators such as sophisticated phishing campaigns, AI-attributed social engineering analyses, and availability of LLM-powered phishing generation tools to evaluate the risk level posed by these advanced social engineering techniques.

What It Detects:

High-quality phishing campaign indicators: Recognizes signals of sophisticated phishing efforts that are likely optimized for engagement through LLMs.
AI-attributed social engineering analyses: Identifies instances where AI is used to analyze and potentially enhance the effectiveness of social engineering content, suggesting a higher level of targeting sophistication.
Availability of LLM-powered phishing generation tools: Detects repositories or platforms that offer tools capable of generating highly personalized and persuasive social engineering content using advanced language models.

Inputs Required:

Domain: The target website’s domain for analysis.
Company Name: The name of the organization associated with the domain to contextualize the detection process.

Business Impact: The deployment of LLM-optimized social engineering techniques poses a significant risk to organizational security, potentially leading to data breaches and other malicious outcomes. This scanner helps in identifying potential vulnerabilities early, enabling proactive measures to mitigate risks.

Risk Levels:

Critical: When the scanner identifies multiple critical indicators such as highly sophisticated phishing campaigns and extensive use of AI for social engineering analysis, the risk level is considered critical.
High: When there are significant indications of LLM optimization in social engineering content, but not as severe as a critical breach, the risk level is high.
Medium: Indicators suggest moderate use of advanced language models in social engineering, posing a medium risk to organizational security.
Low: Minimal indicators of LLM optimization; generally considered low risk unless there are specific contextual factors suggesting otherwise.
Info: Provides informational findings about potential enhancements in social engineering content that could be influenced by LLMs, but does not directly impact critical systems or data.

Example Findings:

A detected high-quality phishing campaign targeting sensitive information with a strong likelihood of being optimized for engagement through LLMs.
AI analysis tools identified as part of an organization’s security framework, suggesting advanced social engineering tactics are in place.

Attacker LLM Usage Prediction

Purpose: This scanner predicts potential attacker usage of Large Language Models (LLMs) by analyzing domain infrastructure and public records for indicators of LLM-enhanced attack capabilities.

What It Detects:

Detection of suspicious infrastructure attack patterns, such as abnormally high certificate issuance rates or semantic typosquatting.
Identification of sophisticated phishing campaigns that leverage highly targeted strategies.
Public demonstrations of offensive capability using tools and models associated with LLMs.
Organization’s defensive awareness regarding AI threats through technical content analysis.
Measures taken by the organization to defend against LLM-enhanced attacks, such as deployment of AI defense tooling.

Inputs Required:

<domain>: The target domain for assessment.
<company_name>: The name of the company associated with the domain.

Business Impact: This scanner is crucial for enterprises and security teams aiming to protect against advanced cyber threats that may utilize LLMs for more targeted and sophisticated attacks. Understanding the potential risk landscape can help in implementing robust defense mechanisms and enhancing overall cybersecurity posture.

Risk Levels:

Critical: When multiple critical indicators are detected, such as high-velocity registration of domains with suspicious activities or public repositories indicating AI defense tooling, it signals a highly vulnerable state that could be exploited by advanced adversaries.
High: Presence of sophisticated phishing campaigns and evidence of offensive capability using LLMs suggests a medium level of risk but is still significant enough to warrant immediate attention for mitigation strategies.
Medium: Indicators of suspicious infrastructure patterns and defensive awareness against AI threats indicate a moderate level of vulnerability that requires proactive measures to enhance security practices.
Low: Minimal indicators of LLM-enhanced attack activity suggest a low risk profile, but ongoing monitoring is recommended to ensure evolving threat landscapes are proactively addressed.

Example Findings:

A domain “examplecorp.com” shows high-velocity registration of subdomains with suspicious activities and evidence of semantic typosquatting, indicating potential infrastructure compromise by an attacker using LLMs for reconnaissance.
Public records reveal a company named “ExampleCorp” actively participating in forums discussing offensive capabilities utilizing LLM models, suggesting preparedness for advanced cyber attacks.

Prompt Engineering Attack Development

Purpose: This scanner monitors the evolution and development of prompt engineering attacks against large language models (LLMs) by analyzing active research streams, company-specific AI attack projects, automated tool developments, and academic publications related to prompt injection vulnerabilities.

What It Detects:

Active prompt injection research repositories that indicate ongoing efforts to exploit LLMs through various techniques like prompt manipulation and adversarial prompting.
Company-specific AI attack research projects that suggest targeted exploitation strategies for the organization’s proprietary models.
Automated prompt attack tools available on GitHub, which are indicators of potential vulnerabilities being exploited or developed by threat actors.
Academic publications on prompt attacks provide insights into theoretical and practical aspects of these vulnerabilities from a broader academic perspective.

Inputs Required:

domain: The target domain for the assessment, e.g., “acme.com”.
company_name: The name of the company whose LLM systems are under scrutiny, e.g., “Acme Corporation”.

Business Impact: The development and exploitation of prompt engineering attacks pose a significant risk to the security and integrity of LLMs used in various industries. Such vulnerabilities can lead to unauthorized access, data manipulation, and potential loss of sensitive information. Understanding and mitigating these risks is crucial for maintaining secure and reliable AI systems.

Risk Levels:

Critical: When multiple active research streams are identified targeting specific organizations’ models, this indicates a critical vulnerability that could be actively exploited by malicious actors.
High: Significant activity in company-specific AI attack projects or the development of automated prompt attack tools suggests a high risk of ongoing exploitation efforts against proprietary LLM systems.
Medium: Moderate levels of research and tool development indicate a medium risk, suggesting that while vulnerabilities exist, they may not be actively exploited at a widespread scale.
Low: Minimal activity in these areas signals low risk, but continuous monitoring is recommended to adapt to potential changes in threat landscapes.
Info: Informational findings provide context on the current state of research and tool development without significant impact on security posture.

Example Findings:

Acme Corporation has been identified as a target for multiple prompt injection research streams, indicating an active campaign to exploit their LLM systems through various techniques.
A specific company-targeted AI attack project suggests targeted exploitation strategies against the organization’s proprietary models, highlighting significant risk associated with this vulnerability.

Vulnerability Analysis Acceleration

Purpose: The purpose of this scanner is to monitor and analyze the potential acceleration of vulnerability research using Large Language Models (LLMs) against a given domain and company. It aims to detect indicators of LLM-accelerated vulnerability research, such as disclosures in public databases, availability of exploit code, development of specialized tools, targeted research on the company’s vulnerabilities, publications related to security advisories, active bug bounty programs, and academic papers discussing AI-assisted vulnerability research.

What It Detects:

Vulnerability Disclosure Indicators: Detection of recent CVE publications or disclosures related to the company.
Exploit Availability Indicators: Identification of public exploit code that targets the company’s products.
Research Tool Indicators: Discovery of AI-powered vulnerability research tools and their development activity.
Company Research Indicators: Projects by third parties researching vulnerabilities specific to the company.
Publication Indicators: Security advisories or publications discussing security issues related to the company.
Bounty Program Indicators: Active participation in bug bounty programs that could be influenced by LLMs.
Academic Indicators: Academic papers or research projects focusing on AI-assisted vulnerability discovery.

Inputs Required:

Domain: The target domain for analysis, which can include websites, APIs, or other digital assets.
Company Name: The name of the company whose vulnerabilities are being researched to determine if LLMs are accelerating this process.

Business Impact: This scanner is crucial as it helps in assessing the risk posed by LLMs in the realm of vulnerability research. By detecting early signs of LLM-accelerated vulnerability research, organizations can take proactive measures to shore up their security posture against potential threats from advanced AI technologies.

Risk Levels:

Critical: If there are indicators of critical vulnerabilities being researched or exploited using LLMs, this poses a severe risk to the organization’s digital assets and could lead to significant damage if not mitigated promptly.
High: High risks are associated with the discovery of public exploits targeting the company’s products, indicating potential exposure to malicious actors who might exploit these vulnerabilities in real-time operations.
Medium: Medium risks pertain to the growing use of AI tools for vulnerability research and could lead to increased complexity in managing security measures against evolving threats.
Low: Low risk is indicated by minimal indicators of LLM-accelerated vulnerability research, suggesting a relatively stable security environment with no significant exposure at this time.
Info: Informational findings are those that do not directly indicate active risks but provide insights into the company’s digital footprint and potential areas for further investigation in the context of AI-driven cybersecurity measures.

Example Findings:

The scanner detects a surge in public CVE disclosures related to Acme Corporation, indicating an increased vulnerability surface being actively researched by third parties using LLMs.
A critical vulnerability affecting Acme’s flagship product is identified as part of ongoing research projects involving LLMs, suggesting potential real-time exploitation risks that need immediate attention from the security team.

LLM-Enhanced Malware Projection

Purpose:
This scanner evaluates the potential for LLM (Large Language Model)-enhanced malware targeting a specific organization by analyzing its domain and company name. It identifies indicators of high-velocity malware distribution, code sophistication, public analysis activity, and acknowledges advanced malware threats to project the likelihood of such attacks.

What It Detects:

Malware Distribution Patterns: Identifies rapid dissemination of malware through various URLs, suggesting a sophisticated and evolving attack vector.
Code Sophistication Indicators: Uncovers evidence of cross-platform code generation and polymorphic signatures indicative of advanced malware development techniques.
Public Analysis Activity: Indicates the presence of public repositories discussing or analyzing potential threats, which could be influenced by LLM capabilities.
Advanced Malware Threat Acknowledgment: Detects mentions in threat intelligence sources about using LLM to enhance malware targeting specific organizations.

Inputs Required:

<domain>: The internet domain name under investigation.
<company_name>: The name of the organization whose security posture is being assessed.

Business Impact:
This assessment is crucial for understanding potential cyber threats and evolving attack methods that could exploit advanced AI capabilities to target specific organizations, potentially leading to significant data breaches or operational disruptions.

Risk Levels:

Critical: When multiple critical indicators are detected along with a high probability of LLM-enhanced malware targeting the organization, indicating imminent severe risk.
High: When there is evidence of advanced malware distribution and acknowledgment of AI influence but no direct threat to critical systems, suggesting significant vulnerability.
Medium: Indicators suggest moderate potential for AI-driven threats without clear impact on core operations.
Low: Minimal indicators of LLM-enhanced malware activity with minimal risk exposure.
Info: Informational findings that do not directly indicate a security threat but may warrant further investigation or monitoring.

Example Findings:

“Acme Corporation” has been identified as having multiple URLs associated with high-velocity malware distribution, suggesting a sophisticated and evolving attack vector potentially enhanced by LLM capabilities.
The company’s public analysis activity indicates discussions about potential threats that could be influenced by advanced AI techniques, which are not publicly acknowledged but suggest significant vulnerability in the cyber defense against such attacks.

LLM Attack Evolution

LLM Attack Evolution

Social Engineering Content Optimization

Attacker LLM Usage Prediction

Prompt Engineering Attack Development

Vulnerability Analysis Acceleration

LLM-Enhanced Malware Projection